Physiological and transcriptomic analysis of tomato in response to sub-optimal temperature stress

ABSTRACT

Tomato (Solanum lycopersicum L.) is one of the most important economic crops in China. However, its quality and yield are susceptible to the adverse effects of low temperatures. In our study, two tomato cultivars, showing different tolerance to low temperatures, namely the cold-sensitive tomato cultivar (S708) and cold-tolerant tomato cultivar (T722), were grown at optimal (25/18°C) and sub-optimal (15/10°C) temperature conditions for 5 days. Our study aimed to explore the effect of sub-optimal temperature on fresh weight, chlorophyll content and chlorophyll fluorescence, soluble sugars and proline content of two tomato cultivars. Moreover, we employed RNA-Seq to analyze the transcriptomic response of tomato roots to sub-optimal temperature. The results revealed that S708 showed a more significant reduction in fresh weight, chlorophyll content, photochemical efficiency of PSII (YII), maximum quantum yield of PSII (Fv/Fm), photochemical quenching (qP) and electron transport rate (ETR) compared to T722 under the sub-optimal temperature condition. Notably, T722 maintained higher level of soluble sugars and proline in comparison to S708 uner sub-optimal temperature. RNA-seq data showed that up-regulated DEGs in both tomato cultivars were involved in “plant-pathogen interaction”, “MAPK signaling pathway”, “plant hormone signal transduction”, and “phosphatidylinositol signaling system”. Furthermore, “Amino sugar and nucleotide sugar metabolism” pathway was enriched only in T722. Moreover, under sub-optimal temperature, transcription factor genes and osmoregulation genes showed varying degrees of response in both tomato cultivars. Conclusion: In summary, our results offer detailed insights into the response characteristics of tomato to sub-optimal temperature, providing valuable references for the practical management of tomato crops under sub-optimal temperature condition.

KEYWORDS:

• Tomato
• sub-optimal temperature
• chlorophyll fluorescence
• transcriptomic

1. Introduction

Low temperature is one of the primary abiotic stress factors limiting the growth and development of plants.¹ Tomato (Solanum lycopersicum L.), as an important economic crop and model plant for scientific research, from tropical and subtropical zones is extremely sensitive to low temperature.² The optimal temperature of tomato growth is (25 ~ 30℃)/20°C (day/night).³ However, the greenhouse tomato production often experiences sub-optimal temperature (8 ~ 12°C to 18 ~ 27℃) in early spring of north China.^4–8 Sub-optimal temperature can disturb the metabolism and physiological balance of plant cells, reduce quality and yield of plants.^9,10 As reported by Hou et al.,¹¹ sub-optimal temperature (15/10℃) increased the content of MDA and the activities of antioxidant enzyme. Venema et al.,¹² found that sub-optimal (16/14℃) day/night temperature reduced plant height and leaf fresh weight of wild Lycopersicon species. Unraveling the underlying mechanism of tomato response to sub-optimal temperature may allow us to improve plant stress tolerance.

Photosynthesis is the basis of dry accumulation and yield production of plants.¹³ Photosynthesis of tomatoes are sensitive to low temperature.^14,15 Different tomato varieties may have different responses when they grow on low temperature.¹⁶ The net photosynthetic rates (P_N) of tolerant tomato varieties were higher than that of sensitive tomato when they suffer low temperature.^14,15 Chlorophylls are vital for photosynthesis, since they can absorb light energy and transfer it to reaction centers.^17,18 Gerganova et al.¹⁹ found that low temperature caused a significant reduction in chlorophyll content of tomato. Chlorophyll fluorescence emission as a noninvasive and effective technique has been introduced in detecting photosystem II (PSII) damage of plants under different abiotic stresses.²⁰ The chlorophyll fluorescence parameters such as maximum quantum effciency of PSII (Fv/Fm), electron transport rate (ETR), photochemical quenching (qP) were well used in physiological studies to examine photosynthetic performance of plants.^20,21 Zhou et al.²² found that the Fv/Fm could be used to evaluate temperature stress tolerance of tomatoes. These parameters have been observed in tomato cultivars during cold stress, but these changes varied associated with genotype, stress level and duration.^15,21

Low temperature has an impact on both the aboveground and underground parts of plants.^23,24 Plant roots are very important for nutrient and water absorption.²⁵ Nagel et al.²⁶ demonstrated that low temperature induced a reduction in root volume. Under low temperature condition, most studies have focused on the physiological response of roots,^5,27,28 but there is little known about the molecular response of roots. Transcriptome sequencing has been used in studying the differential expression of genes (DEGs) at different environmental stress. In tomato, transcriptome sequencing has been used for comparative analysis of genes expression between two tomato cultivars with different resistances to stress.^29,30

In our previous study, the gene expression and K⁺ uptake of two tomato cultivars under sub-optimal temperature were analyzed.³¹ However, the changes of chlorophyll fluorescence parameters, the expression of transcription factors and osmoregulation genes of two tomato cultivars under sub-optimal temperature stress are still not clear. In this study, we determine the growth status of plants by measuring the parameters of the aboveground parts. The root system is in direct contact with the soil environment and is initially subjected to perceived stress and triggers a cascade of signals throughout the plant to obtain sufficient response. Therefore, in our study, sub-optimal temperature tests were conducted on one cold-sensitive and one cold-tolerant tomato cultivar to analyze the difference in their low temperature tolerance. In addition, transcriptome sequencing of two tomato cultivars after low temperature stress was performed to study the expression of transcription factor and osmoregulation genes, which provides a reference for research on genes related to low-temperature tolerance.

2. Materials and methods

2.1. Experimental materials

Different cold-tolerance types of tomato cultivars widely cultivated in Northern China were selected for the experiment, including cold-sensitive tomato cultivar Dongnong708 (S708) and cold-tolerant tomato cultivar Dongnong 722 (T722). The seeds were provided by the Tomato Breeding Center of Northeast Agricultural University in Harbin China. The experiment carried out at Tomato Breeding Center of Northeast Agricultural University in Harbin China.

2.2. Experiment management and treatment

The tomato seeds were placed in plastic pots with dimensions of 10 cm (height)×10 cm (upper diameter) containing a mixture of vermiculite and perlite. All test seedlings were subjected to uniform nutrient and water management before sub-optimal temperature treatment. Sub-optimal temperature experiments were conducted when the seedlings with 4 leaf stage. The S708 and T722 seedlings were grown at a chamber with temperatures of 15/10°C (day/night) as sub-optimal temperature treatment (T). The seedlings were grown at a chamber with temperatures of 25/18°C (day/night) as control (CK). The control and treatment chambers were set 16 h light/8 h dark photoperiod with 480 μmol m⁻² s⁻¹ light intensity and 65% relative humidity.

At 5 days after treatment, the fresh weight of root, stem and leaf, chlorophyll content, chlorophyll fluorescence parameters, soluble-sugars and proline content were measured. At the same time, the tomato roots with or without sub-optimal temperature treatment were collected for RNA-seq analysis.

2.3. Measurement of chlorophyll content

Tomato leaves (0.25 g) were extracted with 10 mL of extraction solution (acetone:95% ethanol = 1:1) and kept overnight. Chlorophyll and carotenoid content were determined by spectrophotometer at 663 nm, 645 nm and 440 nm for chlorophyll a, chlorophyll b and carotenoid, respectively and calculated by the formula described by Arnon.³²

2.4. Measurement of chlorophyll fluorescence parameters

The leaves were put in dark for 30 min using light exclusion clips until measuring chlorophyll (Chl) fluorescence parameters. Chlorophyll fuorescence parameters were measured on the fifth day of the sub-optimal temperature treatments using a IMAGING-PAM (Walz, Germany). After opening the measuring light (0.5 μmol m⁻² s⁻¹) and saturating illuminations (5000 μmol m⁻² s⁻¹), minimum (Fo) and maximum (Fm) fluorescence in the dark-adapted state were measured. Then, opening the light (150 μmol m⁻² s⁻¹) and saturating illuminations, the minimum (Fo^’), maximum (Fm^’) and steady-state fluorescence (Fs) in the light-adapted state were measured. The calculation of maximum PSII photochemical efficiency (Fv/Fm), effective quantum yield of photochemical energy conversion in PSII (Y(II)), photochemical dissipation of absorbed energy (qP) and electron transport rate (ETR) refers to the described by Zhang et al.³³ and Huang et al.³⁴

2.5. Measurement of soluble sugars

The content of soluble sugars in S708 and T722 roots were measured using a method reported by Shi et al.³⁵ 2 mL 80% ethanol was added to 0.1 g sample at 80°C, and kept for 30 min. Then, 2 mL anthrone was added to 100 uL extraction solution, the mixture was heated at 100°C for 10 min. Finally, the solution absorbance was measured at 630 nm.

2.6. Measurement of proline

The proline were measured based on a method reported by Sun et al.³⁶ 0.5 g roots of S708 and T722 were homogenized in 3% sulfosalicylic acid. The homogenates were centrifuged at 12,000 g for 5 min and the supernatant (2 mL) was mixed with an equal volume of glacial acetic acid and acid ninhydrin solutions. Then the mixture were heated at 100°C for 40 min in a water bath before cooling in an ice bath. 4 mL toluene was added into the reaction mixture and then vortexing and incubation at 23 C for 24 h. The toluene layer absorbance was measured at 520 nm.

2.7. Transcriptome data analysis

The transcriptome analysis of the two tomato cultivars plants with or without sub-optimal temperature for 5 days was performed. Total RNA extraction of tomato roots was conducted as described by Gao et al.³¹ After that, the sequencing of these libraries was performed through the Illumina HiSeqTM 2000 system by Shanghai Majorbio Biopharm Technology Co., Ltd. (Shanghai, China). Raw data was analyzed by using SeqPrep (http://gihub.com/jstjohn/) and Sickle (http://github.com/najoshi/), adapter-containing reads, low-quality reads (Q<25) and poly-N reads (with more than 10% N base) were removed to get high-quality clean reads. All raw data for this study were deposited in NCBI Sequence Read Archive (SAR, http://www.ncbi.nlm.nih.gov/Traces/sra) with accession number SRP156519. Differentially expression genes (DEGs) between samples were performed using R package DESeq2. The genes which false discovery rate correction (FDR) p-values <0.05 and log2fold change (log2FC)≥1 were considered as DEGs. The DEGs are listed in the Supplementary Materials (Table S1 and S2).

The functions of DEGs were analyzed through GO and KEGG enrichment using online tool (https://www.omicshare.com/tools/home/soft/getsoft.html). The enriched GO and KEGG terms were confirmed with a threshold of p-value <0.05. The log2(FC) values of DEGs were calculated for generating heat-maps using the pheatmap package of R software. This data is not normalized.

2.8. qPCR analysis of differentially expression genes

The quantitative real-time PCR (qPCR) was conducted to confirm the expression levels of target genes. RNA of tomato roots and leaves was isolated with the Plant RNA Kit (OMEGA bio-tek), after which a SuperRT cDNA kit (CWBIO) was used to synthetize first-strand cDNA. qRT-PCR analysis was performed in a qTOWER real-time PCR system (Analytik Jena, Germany), together with the SuperReal PreMix Plus SYBR Green qPCR Mix (Tiangen). The qPCR cycling condition was as follows: 95°C for 10 min; 40 cycles of 95°C for 10 s, 58°C for 30 s, and 72°C for 40 s. The ACTIN2 was used to normalize the expression level of target genes. qPCR primers used in this study were listed in Table S3. The gene expression was calculated by the 2-^∆∆Ct method.

2.9. Statistical analysis

Statistical differences (p < 0.05) among different treatments were performed according to the Tukey’s honestly in SPSS version 22 (SPSS, Chicago, IL, USA). Data were performed as mean ± standard deviation (SD). Graphs were performed using Origin version 12.0 (Systat, Chicago, IL, USA)

3. Results

3.1. Plant fresh weight

Sub-optimal temperature could induce the growth inhibition of tomato seedlings. The root, stem and leaf fresh weight of T722 were higher than that S708 under control and sub-optimal temperature (Figure 1). Under sub-optimal tempetaure, the decrease of root, stem and leaf fresh weight in S708 (36%, 58%, 38%) were higher than that in S722 (26%, 21%, 22%), respectively Figure 1).

Figure 1. Effect of sub-optimal temperature on morphological and fresh weight of the two tomato cultivars. (a and b) the growth status of tomato seedlings with or without sub-optimal temperature; (c) root fresh weight; (d) stem fresh weight; (e) leaf fresh weight of S708 and T722 cultivars at control (CK, 25/18℃) and sub-optimal temperature (T, 15/10℃). Values are the mean±SD (n=3). Different low letters above the column denote the significant difference according to Tukey’s HSD test (p < 0.05).

3.2. Chlorophyll content

Sub-optimal temperature significantly decreased the chlorophyll content of both cultivars, the magnitude of decreases of chlorophyll a and b content in S708 (19%, 29%) were larger than that in T722 (14%, 18%), respectively (Figure 2a,b). Similarly, under sub-optimal stress, the carotenoid content of S708 decreased significantly (Figure c). Under sub-optimal temperature, there were no significant difference of chlorophyll and carotenoid content between two tomato cultivars (Figure 2).

Figure 2. Chlorophyll content of the two tomato cultivars. (a) Chlorophyll a content; (b) chlorophyll b content; (c) carotenoid content of S708 and T722 cultivars at control (CK, 25/18℃) and sub-optimal temperature (T, 15/10℃). Values are the mean±SD (n=3). Different low letters above the column denote the significantly difference according to Tukey’s HSD test (p < 0.05).

3.3. Chlorophyll fluorescence parameters

Fv/Fm is the maximum quantum yield of PSII, which shows the quantitative efficiency of the quantum yield at all open centers of PSII. Sub-optimal temperature significantly decreased the value of Fv/Fm in S708. However, there was no significant difference in the Fv/Fm value between the control and sub-optimal temperature in T722. Sub-optimal temperature significantly decreased the Y(II) and electron transport rate (ETR) of both cultivars. The magnitude of decreases of Y(II) and ETR in S708 were higher than that in T722. Photochemical quenching (qP) parameter is used for measuring photochemical activity.³⁷ Sub-optimal temperature significantly decreased the qP in S708, but there was no significantly effect on the qP in T722 (Figure 3).

Figure 3. Chlorophyll fluorescence parameters of the two tomato cultivars. (a) maximum quantum yield of PSII, Fv/Fm; (b) effective quantum yield of photochemical energy conversion in PSII, Y(II); (c) photochemical quenching (qP); (d) electron transport rate (ETR). Chlorophyll fluorescence parameters of S708 and T722 cultivars at control (CK, 25/18℃) and sub-optimal temperature (T, 15/10℃). Values are the mean±SD (n=3). Different low letters above the column denote the significantly difference according to Tukey’s HSD test (p < 0.05).

3.4. Soluble sugars and proline content

Sub-optimal temperature significantly increased the soluble sugars and proline content in S708 and T722. There was no significant difference in the soluble sugars and proline content between S708 and T722 under optimal temperature. Under sub-optimal temperature, soluble sugars and proline content in T722 were higher than that in T722 (Figure 4).

Figure 4. Soluble sugars content (a) and proline content (b) of S708 and T722 cultivars at control (CK, 25/18℃) and sub-optimal temperature (T, 15/10℃). Values are the mean±SD (n=3). Different low letters above the column denote the significantly difference according to Tukey’s HSD test (p < 0.05).

3.5. Differential expression gene (DEG) analysis

As shown in Figure 5a, PC1 and PC2 accounted for 56.47% and 19.40% of variance, respectively. Two groups could be distinguished obviously, which meant that there were significantly different between the samples with or without sub-optimal temperature exposure. Based on the global expression profiles, four tomato individuals were clustered into four groups (Figure 5b). The heat-map which showed the correlation between the two groups by clustering analysis also demonstrated the existence of a large number of DEGs between two tomato cultivars (Figure 5c).

Figure 5. PCA analysis and RNA-seq analysis of tomato samples with or without sub-optimal treatment. (a) PCA analysis result (b) heat map showing the pairwise Spearman correlations among different treatments based on expression profiles of all genes (c) heat map for cluster analysis of the DEGs.

3.6. GO and KEGG analysis for DEGs

To determine the function of the up-regulated DEGs identified, GO enrichment was performed using p-value of < 0.05 as the cutoff. Most of the up-regulated DEGs from both tomato cultivars were primarily enriched for “biological regulation” and “regulation of biological process” (Figure 6 a,c). KEGG enrichment analysis showed that the up-regulated DEGs for S708 were mainly enriched the pathways “plant-pathogen interaction”, “MAPK signaling pathway”, “plant hormone signal transduction”, “phosphatidylinositol signaling system” and “vitamin B6 metabolism” (Figure 6 b,d). Among the enriched pathways, four were enriched in both tomato cultivars. Moreover, “amino sugar and nucleotide sugar metabolism” pathway was enriched only in T722 (Figure 6 b,d).

Figure 6. Gene ontology (GO) and KEGG analysis of up-regulated DEGs in both tomato cultivars (a,c) gene ontology (GO) term enrichment analysis of up-regulated DEGs in S708 and T722. The top15 GO term in both cultivars were shown. (b,d) KEGG enrichment analysis of up-regulated DEGs in S708 and T722.

3.7. Analysis of DEGs involved in the low temperature stress responses

In S708 and T722, 50 and 66 TFs in 5 TF families were found to be involved in the responses to sub-optimal temperature stress, respectively (Figure 7a,b). Based on the number of genes in each TF family identified, the ERF and MYB families were the largest classes in response to sub-optimal temperature stress in S708 and T722, respectively, with 13 TFs (7 up- and 6 down-regulated) and 20 (6 up- and 14 down-regulated), respectively, followed by the ZFP family (3 up- and 9 down-regulated) and MYB family (2 up- and 8 down-regulated) in S708, the ERF family (12 up- and 3 down-regulated) and bHLH family (4 up- and 9 down-regulated) in T722 (Figure 7a,b). These genes are shown in Figure 8.

Figure 7. Number of TFs in S708 and T722 under sub-optimal temperature conditions. Within each bar, number of up- and down-regulated genes is shown in orange and blue, respectively. TFs, transcription factors.

Figure 8. Heat map analysis of the DEGs of transcription factors. Changes in the expression levels (represented by the log2FC) of genes are highlighted by color scales (blue to red scale).

One cytoplasmic fructose 1,6-bisphosphatase gene was up-regulated in T722, one fructose-bisphosphate aldolase 2 gene was up-regulated in S708 (Figure 9). One sucrose-phosphate synthase B gene was up-regulated in S708. Moreover, there were one starch synthase 1, one trehalose-phosphate phosphatase and one trehalose-phosphate synthase were up-regulated only in T722 (Figure 9).

Figure 9. Heat map analysis of the DEGs involved in the osmoregulation system. Changes in the expression levels (represented by the log2FC) of genes are highlighted by color scales (blue to red scale).

3.8. Verification of RNA-Seq data

The RNA-seq data was verified through a quantitative real-time PCR analysis of two transcription factor genes and two trehalose related genes in roots and leaves. Our qPCR results suggested that gene expression trends were similar to those from the RNA-seq data. Moreover, we found that the gene expression trend of roots was similar to leaves (Figure 10).

Figure 10. Validation of the RNA-seq expression by qPCR. Values are the mean±SD (n=3). Asterisks means a significant difference (*p < 0.05) with the control.

4. Discussion

Sub-optimal temperature is one of the major constraint factors for tomato growth,³⁸ which can change cell permeability, reduce photosynthetic efficiency and have adverse effects on plant growth.³⁹ In our study, we found that sub-optimal temperature significantly decreased the leaf, stem and root fresh weight in both low temperature sensitive and tolerant cultivars of tomato. The magnitude of decreases of fresh weight in S708 were larger than that in T722. This results indicate that the S708 was more sensitive to sub-optimal temperature than T722. Moreover, we found that the fresh weight of T722 was higher than S708 under control temperature, this indicate that both temperature and cultivars have an effect on plant growth.

Photosynthesis is the basis of dry accumulation and yield production of plants.¹³ Chlorophylls are vital for photosynthesis, since they can absorb light energy and transfer it to reaction centers.^17,18 Low temperature decreases the content of chlorophyll.⁴⁰ The decrease in chlorophyll content can be attributed to the increase of chlorophyll degradation under stress condition.^41,42 In our findings the chlorophyll content of tomato leaves was decreased under sub-optimal temperature stress and such decrease was greater in S708 than in T722. Similarly, previous study also found that there were greater decrease of chlorophyll a and b contents in low temperature sensitive common bean genotype than in tolerant one.⁴³ This indicates that the photosynthetic system of T722 suffered minor damage. Chlorophyll fluorescence emission is a noninvasive, effective, and reliable technique for detecting photosystem II (PSII) damage in plants exposed to abiotic stress environment.²⁰

The Fv/Fm value is typically used to evaluate the stress tolerence of plants.²² In our study, the Fv/Fm of T722 was unaffected by sub-optimal temperature treatment. While the Fv/Fm of S708 was lower at sub-optimal temperature than at control temperature, suggesting that sub-optimal temperature had depressed the photosynthesis of S708. Chlorophyll fluorescence parameters such as qP, Y(II) and ETR are also useful in measuring physiological status of plants under various stress environment.^34,44 Our results found that compared with control temperature, the magnitude of decreases of qP, Y(II) and ETR of S708 at sub-optimal temperature were higher than that in T722. The results suggested that the T722 was in a less photoinhibited state under sub-optimal temperature. Moreover, we analyzed the soluble sugars and proline content associated with the sub-optimal temperature adaptability. In our study, we found a higher level of soluble sugars was observed in the roots of T722 compared to S708. Previous study found that the increase of soluble sugars and proline content under stress condition may help maintain cellular turgor and osmotic balance.^45,46 Similarly, higher accumulation of soluble sugars and proline in the roots of T722 as compared to S708 could also be related to low temperature stress tolerance to T722. Based on the present results, it appears that T722 have a better tolerance to low temperature.

Understanding the molecular mechanism of tomato response to sub-optimal temperature stress will contribute to the study of gene function and the selection of cold resistant varieties. Our study reported the gene expression information of tomato root under sub-optimal temperature stress by RNA-Seq. Heatmap analysis showed that there were many common and unique molecular response mechanisms to cold stress in both tomato cultivars.

GO enrichment analysis of up-regulated DEGs indicated that the “biological regulation” and “regulation of biological processes” were significantly enriched in both tomato cultivars. The results showed that sub-optimal temperature significantly affect the biological processes of tomato roots. Differently, “nuclobase-containging compound biosynthetic process”, “RNA biosynthetic process”, “nucleic acid templated transcription and transcription”, “DNA-templated” were significantly enriched in T722. Huang et al.⁴⁷ suggested that the transcriptome re-program is considered contribute to plant’s base stress tolerance. Up-regulated DEGs associated with “plant-pathogen interaction” and “plant hormone signal transduction” were enriched in both tomato cultivar. The results were consistent with the study reported by Wang et al.⁴⁸ Many sensing and signaling genes are involved in plant-pathogen interaction pathway, thus, our results suggested that the signal sensing and transduction were induced in both tomato cultivars under sub-optimal temperature.⁴⁸ Moreover, we found that amino sugar and nucleotide sugar pathway significantly enriched only in T722. Low temperature tolerance in T722 might be associated with the alteration in the nucleotide sugars biosynthesis.⁴⁹

TFs play important role in regulating gene expression under cold stress.⁵⁰ TFs can regulate cold-responsive genes by binding cis-acting element in their promoters which profit to increase cold tolerance in plants.⁵¹ TFs families, including WRKY, AP2/ERF, MYB and bHLH, which as important regulators involved in plant stress response.^52,53 Among various reported TFs, AP2/ERF played key vital role in improving tomato tolerance. The heterologous expression of JERF1 from tomato in transgenic tobacco and overexpression of LeERF3 in tomato enhance the low temperature tolerance of plants.^54–56 In our study, there were 7 AP2/ERF transcription factors up-regulated in S708, whereas 12 were up-regulated in T722. It can be inferred that AP2/ERF TFs may involve in the cold-tolerance of T722. Moreover, we also found that some other TF families were also found to be differentially expressed, such as MYB, WRKY, and bHLH. Solyc10g086250.1, which encodes SlMYB113-like of tomato, positively regulates the low temperature tolerance of tomato.⁵⁷ This gene was induced by sub-optimal temperature in S708, which suggested that its up-regulation might contribute to low temperature stress tolerance. Moreover, SlbHLH96-like gene (Solyc11g010340.1) was induced in both tomato cultivars. Liang et al.⁵⁸ found that SlBHLH96 promote abiotic tolerance of tomato. These results suggested that numbers of TFs regulated plant low temperature response through different pathways.

Osmotic regulatory substances, such as soluble sugar, protein and proline, can maintain or reduce cell osmotic potential and enhance water holding capacity under abiotic stress.^59,60 Moreover, it has been reported that these substances also play a major role in maintaining the stability of cell membranes by activating reactive oxygen species (ROS) scavenging systems.¹¹ In this study, we found some DEGs involved in soluble sugars accumulation. Among them, fructose-bisphosphate aldolase genes were up-regulated in both tomato cultivars. Differentially, one sucrose-phosphate synthase was up-regulated only in S708 and the gene involve in the sucrose synthesis pathway in plants.⁶¹ Interestingly, we found that the genes involved in the processes of trehalose synthesis were up-regulated only in T722, which encoding the trehalose-6-phosphate phosphatase (TPP) and trehalose-6-phosphate synthase (TPS), respectively.⁶² Mollavali and Börnke⁶² found that most of tomato of TPS/TPP genes were induced by temperature stress, suggesting that trehalose synthesis pathway may play important role in plants temperature stress adaptation. Trehalose forms a special protective structure on the cell membrane surface to protect the activity of protein molecules under abiotic stress.⁶³ These results suggested that S708 and T722 may improve plant cold resistance by regulating different osmotic substances under low temperature stress. Proline is emerging as a critical osmotic adjustment substance of low temperature stress.⁶⁴ Proline dehydrogenase can catalyze proline degradation.⁶⁵ Transgenic tobacco lines with partial proline dehydrogenase gene suppression increased content of proline showed greater resistance to low temperature stress.⁶⁶ Our previous studies found that proline dehydrogenase genes were down regulated in two tomato cultivars may contribute to improve the cold tolerance of plants.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15592324.2024.2332018

Additional information

Funding

This work was supported by the Basic Scientific Research Foundation of Heilongjiang Provincial Universities 2023-KYYWF-1501.