Transcriptomic approach to address low germination

South African Journal of Botany 119 (2018) 286–294

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Transcriptomic approach to address low germination rate in Cyclobalnopsis gilva seeds M. Zaynab a, D. Pan a, M. Fatima c, S. Chen b,⁎, W. Chen a,⁎ a b c

College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, PRChina College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, PRChina College of Crop Science, Fujian Agriculture and Forestry University, Fuzhou 350002, PRChina

a r t i c l e

i n f o

Article history: Received 20 June 2018 Received in revised form 2 September 2018 Accepted 19 September 2018 Available online xxxx Edited by I Demir Keywords: Cyclobalnopsis gilva Carbohydrate metabolism Glycolysis and TCA cycle Sucrose and starch metabolism

a b s t r a c t Cyclobalnopsis gilva is a woody plant with lower seed germination rate in its natural habitats as compared with other plants. The regulation at transcriptional level to of seed germination in C.gilva is yet to be discovered. Illumina HiSeq 2500 platform was performed to investigate the transcriptome of germinated and nongerminated seeds of C.gilva. A total of 40,782 differentially expressed unigenes (DEGs) were identified in germinated and non-germinated seeds and compared with the control. Carbohydrate metabolisms were enriched significantly through the analysis of KEGG pathways of DEGs. The physiological parameters and mRNA expression levels relevant to carbohydrate metabolisms including glycolysis and TCA cycle, and sucrose and starch metabolism were found to be poor in non-germinated seeds. The consistent results of the transcriptomic data, physiological analyses, and mRNA expression levels suggest that C.gilva seeds fail to germinate due to the lower activity of carbohydrate metabolism. Our findings will expand the knowledge on the transcriptional responses of respiratory pathways, sucrose, and starch metabolism related to lower seed germination rate of C.gilva. © 2018 SAAB. Published by Elsevier B.V. All rights reserved.

1. Introduction Seed germination is the first important stage in the plant life cycle (Gioria et al., 2016). In general, seed germination is described as a process including three phases: Phase I is a rapid initial phase, namely seed imbibition during which storage starch and protein in the embryo changes gradually, thus glycolysis and anaerobic respiration begin to occur; Phase II is a plateau phase during which the uptake of water results in an increased oxygen supply, hence mitochondria becomes activated; Phase III is the post-germination phase during which the embryonic axes elongate along with the radicle development (Gimeno-Gilles et al., 2009). Energy production plays vital roles in seed germination. Energy is mainly generated by anaerobic respiration in the beginning of seed germination and subsequently by aerobic respiration along with an intensification of water uptake (Pergo and Ishii-Iwamoto, 2011). Physiological processes during germination require considerable energy (Bao et al., 2017) and the main energy source for cellular metabolism is glucose, which is catabolized by subsequent processes—glycolysis and tricarboxylic acid (TCA) cycle, and finally oxidative phosphorylation to produce ATP. Energy production pathways like respiration are important in the whole-seed germination by providing required ATP. ⁎ Corresponding authors. E-mail addresses: [email protected] (S. Chen), [email protected] (W. Chen).

https://doi.org/10.1016/j.sajb.2018.09.024 0254-6299/© 2018 SAAB. Published by Elsevier B.V. All rights reserved.

Seed germination is highly correlated with seedling survival rate as well as subsequent seedling growth and development, thus directly affecting the quality of the seedlings. Studies on seed germination have mainly focused on aspects of seed physiology and biochemistry (Noman et al., 2015). It is reported that major physiological changes and reactivation of metabolic processes take place during seed germination (Rajjou et al., 2006; Holdsworth et al., 2008; Zaynab et al., 2017). ATPases are synthesized to support normal cellular function during the late phase of germination (Chen and Bradford, 2000; Mei and Song, 2010). Transcriptomic analysis indicated that the enzymes in TCA cycle are reported to be up-regulated after imbibition (Yao et al., 2016). We have a basic understanding of energy provision in seed germination, but how those gene expressions are related to energy production pathways regulating seed germination is largely unknown (He and Yang, 2013; Mangrauthia et al., 2016). High-throughputRNAsequencing (RNA-seq) method is opening new horizons in the field of transcripts participated in specific biological metabolic processes (Mangrauthia et al., 2016). Over the years, model plant species such as Oryza sativa, barley, maize, and Arabidopsis thaliana have been analyzed through transcriptomic studies with respect to seed germination (Rajjou et al., 2004; Nakabayashi et al., 2005; Radchuk et al., 2007; Kimura and Nambara, 2010; Okamoto et al., 2010). A daunting challenge is to provide detailed transcriptomic profiling of germination process in woody plants. Cyclobalnopsis gilva, belongs to Fagaceae, a woody plant. It is native to East Asia and abundantly found in Japan and China.

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The furniture industry in the region prefers its wood due to its hardness. Seeds from C.gilva have a long quiescence, so sand-burying can break the dormancy of the quiescent seeds to promote germination. As compared with other plants in the same habitat and region, the germination rate of C.gilva seed is very low (less than 50%) affecting the seedling survival (Zaynab et al., 2017). Transcriptomic study of C.gilva will give new insights into the genes involved in energy metabolism. The current study was carried out to understand transcriptional responses to low germination rate in C.gilva seeds. This study will serve as a public information and resource for future functional genomic and genetic studies on the seed germination of C.gilva.

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used to gather the linear contigs and construct de Bruijn graphs. Transcripts were assembled from all the generated contigs. Lastly, the butterfly module was used to analyze the de Bruijn graphs and generate transcript sequences. The main transcripts made up of more than 200 bp were selected as unigenes. BLASTX alignment (Altschul et al., 1997) was carried out between the generated unisequences and the public protein databases: non-redundant (Nr) (Deng et al., 2006), Swiss-Prot, Clusters of Orthologous Groups (COGs), Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa et al., 2004), and Gene Ontology (GO) protein database (Ashburner et al., 2000). 2.4. Analysis of quantitative real-timePCR (qRT-PCR)

2. Materials and methods 2.1. Plant materials Cyclobalnopsis gilva seeds were collected from Fujian province, China. Seeds of C.gilva were put in a container of water. Seeds sank in water were considered as quality seeds and were collected to do germination experiments, while floating seeds were removed. Five hundred seeds were selected for the experiment. Seeds were sown in wet sand (2 m length × 1 m width × 0.5 m height) in an open field with white plastic film roof (4 m length × 2 m width × 1 m height) for 60 d with an average temperature of 16 °C and a humidity of 60%, which is the favorable condition for C.gilva to grow from December 2016 to February 2017. The investigation of seed germination rate was calculated after 60-d wet sand treatment. Three biological replicates were conducted for this study. Each replicate (30 seeds), including control seeds (seeds not sown in wet sand), germinated seeds (namely SG), and nongerminated ones (namely NG), was collected and immediately immersed in liquid N2 and stored at −80 °C for future experiments. 2.2. RNA extraction and construction of cDNA libraries for transcriptome sequencing Total RNA from C.gilva seed tissue was extracted through a modified CTAB method (Chan et al., 2007). Agilent 2100 bioanalyzer was employed to assess the integrity of the total extracted RNAs. Two cDNA libraries were constructed, and sequencing of transcriptomes was carried out by Biomarker Technologies Co., Ltd. (Beijing, China). For purification and enrichment of mRNAs from the total RNAs of each sample, oligo (dT) magnetic beads were used according to instructions of Illumina manufacturer. The enriched mRNAs were short fragmented, and these fragments were reverse transcribed for the first and the second strands cDNA synthesis. Afterward, adopters were ligated with these double-stranded fragments and further appropriate DNA fragments were used as templates for PCR amplification. 2.3. Illumina sequencing, assembly, and annotation Sequencing of cDNA libraries was carried out using Illumina HiSeq™ 2500 and raw 100 nt paired end reads were generated. To filter out the reads, quality parameters including sequence duplication level, GCcontents, Q20, and Q30 were used and low-quality reads. The reads containing poly-N and those containing adapter were eliminated, and the high quality clean reads were obtained from the raw reads. De novo transcriptome assembly of clean reads was executed using the Trinity assembly program with default parameters (Grabherr et al., 2011). Trinity software contains three components: Inchworm, Chrysalis and Butterfly (http:// trinityrnaseq.sourceforge.net/). Initially, Inchworm created a k-mer dictionary by breaking all sequence reads (“k-mer,” a sequence of a fixed length of k nucleotides, in practice, k = 25 bp). After removing error-containing, low-complexity and singleton k-mers, the most frequent k-mer was selected for a contig assembly. The contigs were obtained until the two sides of the sequence could not be extended with a k-1 overlap. Afterward, Chrysalis module was

Total RNA from C.gilva seeds was extracted through a modified CTAB method (Chan et al., 2007). According to the transcriptomic data, primers were designed by using Primer Premier 5.0 software (Supplementary Table 1). RT-qPCR was conducted using SYBR Premix Ex Taq™ (Tli RNaseH Plus) kit (TaKaRa, Japan) at 95 °C for 30 s, followed by 40 cycles at 95 °C for 5 s, and 60 °C for 30 s. The whole thermocycling process was conducted in a BioRad CFX96 real-timePCR detection system. Actin gene was chosen as the internal standard to normalize gene expression. RT-qPCR was performed in three replicates of all samples. The 2−ΔΔCt method was employed to compute the relative quantitative gene expression (Livak and Schmittgen, 2001). 2.5. Analysis of physiological parameters The activities of α-amylase, β-amylase, PFK, PGK, PK, and NAD-MDH were evaluated using commercial reagent kits from Suzhou Comin Biotechnology Co., Ltd. (Suzhou, China) according to instructions from the manufacturer. Soluble sugar and starch contents were assayed by adopting the method described by Tollenaar and Daynard (1978). Each data point represents an average ± standard deviation (SD) of three repeated experiments. 2.6. Statistical analysis Analysis of variance (ANOVA) was used to statistically analyze the data. The least significant difference (LSD) was applied to determine the significant differences among group means at P b 0.05. 3. Results 3.1. Sequence analysis and assembly from C.gilva seeds To break dormancy, seeds of C.gilva were germinated in wet sand. The germination rate of C.gilva seeds was found to be as low as 37.68% in this study (Supplementary Table 2). Therefore, transcriptome analysis of C.gilva seeds was performed to understand the transcriptional changes in C.gilva during seed germination. After removing lowerquality reads, more than 6.77 billion clean reads with acceptable quality were generated in three samples (Table 1). After cleaning and quality checks, Q30 and GC percentages exceeded 94% and 43%, respectively (Table 1). 3.2. Analysis of differentially expressed unigenes (DEGs) by RNA-Seq Compared with the control in the present study, a total of 40,782 unigenes were differentially expressed in the SG and non-germinated ones NG, among which 8146 unigenes were up-regulated while 8951 unigenes were down-regulated in SG samples, and 14,260 unigenes were up-regulated while 9425 unigenes were down-regulated in NG samples (Table 2). After sequencing of transcriptome gene ontology was carried out on the basis of biological processes, molecular functions, and cellular components, GO analysis and annotation were done using three categories

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3.4. Physiological profiles during seed germination

Table 1 Summary of sequencing output statistics of C.gilva seeds. Sample

Total clean Reads

Total clean nucleotides (nt)

GC (%)

Q30 (%)

Control NG SG

7,807,981,763 6,775,638,348 7,557,362,739

8,285,457,053 7,199,609,164 8,033,719,902

45.34 43.96 44.95

94.24 94.11 94.07

containing biological process, molecular function, and cellular component. In many cases, the numerous GO terms were assigned to the same unigene. All DEGs were categorized into many functional subgroups related to biological processes, molecular functions, and cellular components (Fig. 1). In both groups of Control-SG and Control-NG, the DEGs implicated in metabolic and cellular processes were plentiful in the biological processes, the ones involved in binding and catalytic processes were dominant in the molecular functions, and the ones in the cell and cellular parts were abundant in the cellular components. Kyoto Encyclopedia of Genes and Genomes (KEGG) is a metabolic pathway database that can be employed to examine the function and products of genes in cellular processes. KEGG pathway analysis is used to logically understand the complex biological performance of genes in the form of networks. For KEGG pathway analysis, Blast all software was used in comparison with the KEGG database. The tools of KEGG pathway were used to analyze the metabolic pathways associated with the DEGs in C.gilva seeds. In consideration of the P-value threshold of 0.05, a total of 31 pathways were significantly enriched in the Control-SG group (Supplementary Table 3). These enriched pathways were associated chiefly with carbohydrate metabolism, such as starch and sucrose metabolism, pentose phosphate pathway, glycolysis/gluconeogenesis, and glucuronate interconversions. The carbohydrate metabolism pathways are of particular consideration due to their correlation with energy production for seed germination. Similarly, in the Control-NG group, DEGs implicated in carbohydrate metabolism (like starch and sucrose metabolism, pyruvate metabolism, pentose phosphate pathway, glycolysis/gluconeogenesis) were identified to be significantly enriched (Supplementary Table 4), which is the main objective of this study.

3.3. mRNA expression level during seed germination Expression profiles of some DEGs involved in starch and sucrose metabolism, glycolysis, and TCA cycle were created by performing RTqPCR(Fig. 2). Compared with control, the mRNA expression levels of genes including beta-glucosidase(BGL), phosphofructokinase (PFK), pyruvate kinase (PK), alcohol dehydrogenase (ADH), enolase 1 (ENO1), Isocitrate lyase (ICL), malate dehydrogenase (MDH), β-amylase(BAM), sucrose synthase 6 (SuSy6), sucrose-phosphate synthase (SPS), and ATP synthase (ATPase) showed no changes or down-regulations in NG samples, but they were up-regulated in SG samples. The mRNA expression level of phosphoglycerate kinase (PGK) was up-regulated both in NG and SG samples. However, the relative expression level of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was downregulated in the NG sample but showed no changes in that of SG. The results from RT-qPCR analysis were consistent with those from the RNAseq data (Table 3), which validated the expression profile of the DEGs.

Table 2 Differentially expressed genes in C.gilva seeds. Groups

Up-regulated differential genes

Down-regulated differential genes

All differential genes

Control-VS-NG Control-VS-SG

14,260 8146

9425 8951

23,685 17,097

In the present study, we measured the activities of key enzymeslikeα-amylase, β-amylase, Phosphoglycerate (PGK), Phosphophofructokinase (PFK), Pyruvate Kinase (PK), and NAD-malate dehydrogenase (NAD-MDH). As shown in Fig. 3, the activities of PFK, PGK, PK,NAD-MDH, and β-amylase were changed insignificantly or down-regulated in NG samples, but they were all up-regulated in SG samples compared with control. However, the activity of α-amylase was shown to have no changes in NG samples, but it was downregulated in those of SG compared with control. Comparative analyses of the control, NG and SG samples were carried out to determine soluble sugars and starch content. Significant differences were observed among all replicates of the mentioned categories of seeds. Compared with the control, the soluble sugar content was decreased, and the starch content showed no changes in NG samples, but they were both decreased in those of SG(Fig. 3). 4. Discussion Successful seed germination is a very important step in plant life and it depends on both genetic makeup and the environment surrounding the germinating seed. Many important metabolic pathways are involved in plant seed germination. In the present study, we found that the decreased activity of carbohydrate metabolism including glycolysis and TCA cycle, and starch and sucrose metabolism at transcriptional level was closely related to the low rate of seed germination in C.gilva. 4.1. Transcriptional responses in the glycolysis and TCA cycle pathway Seeds can generate energy through respiratory pathways i.e. glycolysis and TCA cycle in the process of germination. In the present study, many genes related to carbohydrate metabolism were identified to be enriched during the seed germination of C.gilva, which were downregulated or showed insignificant changes in NG samples, and mostly up-regulated in the SG samples according to the RNA-seq and RTqPCR data (Fig. 2 and Table 3). In the process, the genes encoding key enzymes in glycolysis such as PFK, PGK, PK, and GAPDH displayed down-regulation or no changes in the NG samples, and up-regulated expression in those of SG, which were supported by Yao et al. (2016) and Bellieny-Rabelo et al. (2016). In addition, previous reports found that the glycolytic enzymes, glycolysis rate, and energy production were the major determinants of a successful rice seed germination (Yang et al., 2007). Correspondingly, the activities of PFK, PGK, and PK, key enzymes involved in the glycolysis pathway, were changed insignificantly or down-regulated in NG samples, while they were all up-regulated in SG samples during seed germination of C.gilva(Fig. 3). It is reported that pyruvate decarboxylase (PDC) and alcohol dehydrogenases (ADH) in ethanol fermentation are significantly up-regulated during rice seed germination (Yu et al., 2014). Similarly, our study also found that the mRNA expression level of ADH was up-regulated in SG samples, but down-regulated in NG samples. These findings suggested that both glycolysis and ethanol fermentation pathways were down-regulated in NG samples due to unchanged or down-regulated expressions of key genes together with unchanged or down-regulated enzyme activities involved in glycolysis and ethanol fermentation. Pyruvate, the end product of glycolysis, is converted into acetyl coenzyme A by pyruvate dehydrogenase complex (PyDC) in TCA cycle and becomes a power source for mitochondrial electron transport chain for ATP production (Weitbrecht et al., 2011; Li et al., 2017). PyDC is a multi-component complex including pyruvate dehydrogenase, dihydrolipoyl transacetylase (DLAT), and dihydrolipoamide dehydrogenase in plants (Zhang et al., 2017). During the seed germination of C.gilva, the transcriptional levels of PyDC and DLAT were downregulated in NG samples but up-regulated in SG ones. Isocitrate is broken down into succinate and glyoxylate by ICL(Fig. 4). Many reports

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Fig. 1. GO categorization of unigenes from C. gilva seeds.

reveal the gene expression level of ICL responsible for the energy production in germinating seeds (Gonzalez and Vodkin, 2007; Yao et al., 2016). Similarly, the mRNA expression level of ICL in SG samples was up-regulated and down-regulated in NG ones during the seed germination of C.gilva. MDH is an important enzyme for NADH production in the TCA cycle. Concurrently, the increment in transcripts levels (Sreenivasulu et al., 2008) and enzyme activities linked with the TCA cycle are reported in the seed germination. Among all the enzymes in the TCA cycle, the activity of MDH is extremely high in germinated seeds (Soeda et al., 2005; Morohashi, 1986). It was found in our study that the genes encoding MDH in TCA cycle pathway were upregulated in SG samples and down-regulated in NG ones, which is supported by Yao et al. (2016). Taken together, the transcriptional levels and enzyme activities in the TCA cycle were down-regulated in NG samples but up-regulated in SG ones. As we know, ATP synthesis metabolism is restored through ATPase. It is reported that increased mRNA expression level of ATPase can contribute to ATP formation (Pan et al., 2018). In the present study, correspondingly, the unchanged or downregulated mRNA expression level of ATPase might not be conducive to

form ATP in NG samples. These findings indicated that the up-regulations of genes and enzymes in the glycolysis and TCA cycle pathway contributed to seed germination, but their down-regulations in NG samples may not provide sufficient energy for seed germination leading to failed germination in C.gilva seeds. Therefore, the connection between the germination of C.gilva seed, the glycolysis, and TCA cycle is proven to exist. Furthermore, PFK, PGK, PK, MDH, and ICL are the key genes in the pathway to regulating C.gilva seed germination. 4.2. Transcriptional responses in starch and sucrose metabolism The energy required for seed germination is derived from stored starch and fat, even sometimes from protein (Yao et al., 2016). Starch is the primary energy resource for seed germination. Starch, stored polysaccharide in seeds, has an important role in the seed germination. The ultimate purpose of starch mobilization is to provide energy (Weitbrecht et al., 2011). The unit monomers of starch enter glycolysis in two ways. First, starch is converted into maltose by amylases and further broken down into glucose which enters glycolytic metabolism.

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Fig. 2. RT-qPCR analyses of differentially expressed genes from the C.gilva seeds. Thirteen differentially expressed genes were selected for the quantitative RT-PCR analysis. Standard error of the mean for three technical replicates is represented by the error bars. The results represent the mean (±SD) of the three independent biological replicates. Actin gene was chosen as an internal standard. BGL, beta-glucosidase; PFK, phosphofructokinase; PGK, phosphor-glyceratekinase; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; PK, pyruvate kinase; ADH, alcohol dehydrogenase; ENO1, enolase 1; ICL, isocitrate lyase; MDH, malate dehydrogenase; BAM, β-amylase; SuSy6, sucrose synthase 6; SPS, sucrose-phosphate synthase; ATPase, ATP synthase.

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291

Table 3 The key candidate genes related with carbohydrate metabolism responding to the germination of C.gilva seeds. Gene ID

GENE

Description

Species

log2 ratio log2 ratio (SG/Control) (NG/Control)

Unigene0014803 Unigene0026403 Unigene0033670 Unigene0053933 Unigene0052472

NAD-GAPDH GAPDH GAPDH GAPDH GAPDH

NAD-dependent glyceraldehyde-3-phosphate dehydrogenase glyceraldehyde-3-phosphate dehydrogenase Glyceraldehyde-3-phosphate dehydrogenase Glyceraldehyde-3-phosphate dehydrogenase A Glyceraldehyde-3-phosphate dehydrogenase

2.332295 5.299021 1.57829 5.347194 2.065446

Unigene0040316 PGDH

D-3-Phosphoglycerate

dehydrogenase

Asplenium monanthes Tarenaya hassleriana Pyrus x bretschneideri Cucumis sativus Coccomyxa subellipsoidea C-169 Morus notabilis

2.566513

0.298028

Unigene0021707 PGDH3

D-3-Phosphoglycerate

dehydrogenase 3

Nelumbo nucifera

1.368255

−1.11222

Unigene0028459 Unigene0028827 Unigene0028866 Unigene0040235 Unigene0031354 Unigene0031355 Unigene0030377 Unigene0003535 Unigene0031356 Unigene0003027 Unigene0036539 Unigene0021320 Unigene0005799 Unigene0029666 Unigene0027524 Unigene0006895 Unigene0041330 Unigene0018789 Unigene0034565 Unigene0034566 Unigene0018106 Unigene0041816 Unigene0049807 Unigene0034986 Unigene0034987 Unigene0005753 Unigene0006545

GALM NDU PGK PGK 6-PFK PFKPFK PFK PFK3 ENO1 PK FBA PEPCK ALDH ALDH ADH ADH ADH ACAT ACAT ACS PDC1 PDC PDC PDC MT-ND DLAT

Prunus mume Tarenaya hassleriana Malus domestica Cucumis sativus Morus notabilis Theobroma cacao Hevea brasiliensis Citrus x paradise Theobroma cacao Prunus mume Elaeis guineensis Camellia oleifera Nelumbo nucifera Malus domestica Glycine soja Quercus suber Paeonia lutea Jatropha curcas Populus euphratica Camelina sativa Ectocarpus siliculosus Citrus sinensis Aegilops tauschii Malus domestica Morus notabilis Theobroma cacao Morus notabilis

1.731711 2.457374 1.422125 1.776038 1.414865 1.80522 1.24477 1.56332 1.392088 1.03040994 1.003788 2.161238 1.375567 2.499247 1.591904 2.764359 2.608049 1.5166881 1.310109 1.241959 11.54352 3.268592 11.84353 1.764253 1.739171 1.244911 1.085056

0.406712 −0.93632 −1.58668 0.417257 −2.76935 −0.65708 −1.4831 0.892865 −2.84306 −0.81119 0.75047 −2.58865 0.069448 −2.32653 −1.90726 −0.29606 0.127761 −0.46004 −2.52115 −0.19942 – 0.658057 – 0.050082 0.992715 −0.02595 0.438348

Unigene0048815 Unigene0036426 Unigene0039652 Unigene0020726 Unigene0047047 Unigene0018300 Unigene0050177 Unigene0043179 Unigene0024101 Unigene0025067 Unigene0030234 Unigene0050997 Unigene0006554 Unigene0048474 Unigene0005363 Unigene0007281 Unigene0034767 Unigene0012425 Unigene0004705 Unigene0018780 Unigene0014879 Unigene0052377 Unigene0053596 Unigene0005070 Unigene0006123 Unigene0008692

PyDC MDH MDH ICL ICL ATPase ATPase ATPase ATPase ATPase ATPase CS UGDH BGL17 BGL BGL FK4 SuSy7 SuSy6 SuSy6 SuSy SuSy SuSy3 SS SPS4 BAM

Aldose 1-epimerase NADH dehydrogenase ubiquinone Phosphoglycerate kinase Phosphoglycerate kinase 6-Phosphofructokinase Phosphofructokinase Phosphofructokinase Phosphofructokinase Phosphofructokinase 3 Enolase 1 Pyruvate kinase Fructose-bisphosphate aldolase 3 Phosphoenolpyruvate carboxykinase Aldehyde dehydrogenase Aldehyde dehydrogenase Alcohol dehydrogenase Alcohol dehydrogenase Alcohol dehydrogenase Acetyl-CoA acetyltransferase Acetyl-CoA acetyltransferase Acetyl-coenzyme A synthetase Pyruvate decarboxylase Pyruvate decarboxylase Pyruvate decarboxylase Pyruvate decarboxylase NADH–ubiquinone oxidoreductase Dihydrolipoyllysine-residue acetyltransferase component of pyruvate dehydrogenase Mitochondrial pyruvate dehydrogenase complex Malate dehydrogenase Malate dehydrogenase Isocitrate lyase Isocitrate lyase ATP synthase subunit g ATP synthase gamma chain ATP synthase ATP synthase subunit b ATP synthase subunit beta ATP synthase delta chain Cellulose synthase UDP-glucose 6-dehydrogenase Beta-glucosidase 17 Beta-glucosidase Beta-glucosidase Fructokinase-4 Sucrose synthase 7 Sucrose synthase 6 Sucrose synthase 6 Sucrose synthase Sucrose synthase Sucrose synthase 3 Starch synthase Sucrose-phosphate synthase 4 Beta-amylase

Chlamydomonas reinhardtii Populus euphratica Cicer arietinum Eucalyptus grandis Ectocarpus siliculosus Gossypium arboreum Eucalyptus grandis Citrus sinensis Theobroma cacao Solanum lycopersicum Eucalyptus grandis Populus trichocarpa Vitis vinifera Jatropha curcas Jatropha curcas Jatropha curcas Prunus mume Vitis vinifera Hevea brasiliensis Populus euphratica Betula luminifera Orobanche aegyptiaca Betula luminifera Aegilops tauschii Citrus sinensis Castanea crenata

11.42731 1.363915 1.4577 1.747066 11.66436 1.376335 2.623921 1.143138 1.273157 11.12806 1.543899 10.94405 1.547078 1.8728 1.887657 2.054071 1.651518 2.960241 3.573566 4.79174 2.872698 11.93679 1.194706 1.158088 3.925826 3.296334

– −1.04743 −1.65263 −4.89373 – −1.207 −2.84347 −1.22538 −1.76267 – 0.268663 – 0.906666 −8.78202 −10.1018 −2.7439 −0.67214 −7.54458 −7.30195 −6.90809 −1.52073 – −3.39615 −0.23592 0.741619 −2.0576

Second, starch phosphorylase can convert starch into glucose-1phosphate for entry into glycolysis. Amylases convert stored starch into amylose to release energy for germinating the seed. β-Amylase gets activated in the second phase of seed germination while αamylase is activated by GAs signaling cascade(Muralikrishna and Nirmala, 2005; He and Yang, 2013). β-Amylase can catalyze starch to glucose and fructose which are further used for structural, developmental purpose. In the current study, the pragmatic approach was adopted

0.64867 0.594116 0.709055 −8.14109 −2.74304

to report that there was no obvious link between seed germination and α-amylase activity, but β-amylase was found to have a significantly higher activity and mRNA expression level during the seed germination of C.gilva., which is consistent with a previously established observation (Yamasaki, 2003). These resulted in a corresponding depletion of starch contents in the germinated seeds due to an accelerated digestion of starch with the increased β-amylase activity and mRNA expression level in the germinated seeds of C.gilva. However, NG samples have no

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Fig. 3. Analysis of physiological parameters in C.gilva seeds. Values (mean ± SD) were measured from three independent experiments (n = 3). Different letters above the bars indicate a significant difference at P b .05.

changes in starch content so that the starch will not degraded to provide carbohydrate for energy generation in NG ones. Two enzymes, SPS and SuSy, play important roles in sucrose biosynthesis and metabolism. Sucrose can be changed into fructose and glucose by SuSy, which also enters glycolysis. SuSy is linked to the reversibility of sucrose to its monomers and hence they are involved in providing hexoses and UDP-glucose for energy production (Miransari and Smith, 2014). SPS is a key enzyme regulating sucrose biosynthesis in plants. The activities of SuSy and SPS were positively correlated with their transcriptional levels to accumulate carbohydrate in

mature sugarcane internodes, respectively (Verma et al., 2011). In the present study, the transcriptional levels of SuSy and SPS were found to be down-regulated in NG samples, but up-regulated in SG ones (Fig. 2). Sucrose and starch contents were decreased in SG samples, suggesting that the depletion of starch and sucrose was higher in germinating seeds. Hence it was revealed that starch mobilization occurs in germinating seeds but not in non-germinating seeds, which may be the cause of low seed germination rate in C.gilva seed (Sreenivasulu et al., 2008). Similar with the report of BellienyRabelo et al. (2016), the genes encoding cellulose synthase were

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SuSy6

293

CS

Sucrose

UDP Glucose

Cellulose

Glucose Glucose 6-P Fructose 6-P

PFK Fructose 1,6-BP

FBA G3P

GAPDH 1,3-bisphosphoglycerate

PGK 3-phosphoglycerate 2-phosphoglycerate

ENO1 PEP

PK

ADH

PDC

Ethanol

Acetaldehyde

Pyruvate

Acetyl-CoA

Citrate

Oxaloacetate

Cis-Aconitate

MDH Malate

ICL Glyoxylate

Isocitrate

α-Ketoglutarate

Fumarate

Succinate

Succinyl CoA

Fig. 4. Schematic presentation of biological pathways involved in the germination of C.gilva seeds. Differentially expressed genes (DEGs) were integrated. DEGs with a red box were upregulated in germinated seeds, DEGs with a green box were down-regulated in non-germinated seeds, and DEGs with a blue box showed insignificant changes in non-germinated seeds compared with control. SuSy6, sucrose synthase 6; CS, cellulose synthase; PFK, phosphofructokinase; FBA, fructose-bisphosphate aldolase; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; PGK, phosphoglyceratekinase; ENO1, enolase 1; PK, pyruvate kinase; PDC, pyruvate decarboxylase; ADH, alcohol dehydrogenase; ICL, isocitrate lyase; MDH, malate dehydrogenase; Glucose 6-P, Glucose 6-phosphate; Fructose 6-P, fructose 6-phosphat; Fructose 1,6-BP, fructose 1,6-bisphosphate; G3P, glyceraldehyde 3-phosphate; PEP, phosphoenolpyruvate. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

found up-regulated in SG samples compared with the control, but they were down-regulated in NG ones. Taken together, the poor activity of starch and sucrose metabolism in NG samples fails to provide enough carbohydrate for the glycolysis and TCA cycle in the germination of C.gilva seeds. 5. Conclusion This study was aimed to investigate the regulation at the transcriptional level in SG and NG seeds by exploring the regulating factors of low germination rate of C.gilva. From RNA seq data, DEGs were classified into different groups according to their functions during the seed germination of C.gilva, among which carbohydrate metabolism including glycolysis and TCA cycle, as well as sucrose and starch metabolism were

enriched significantly. Genes PFK, PGK, and PK of glycolysis pathway and MDH and ICL of TCA cycle were up-regulated in SG seeds or down-regulated or no changes in NG samples of C.gilva seeds. Together with the similar observation of key enzyme activities in the pathways, the transcriptional levels of genes encoding the key enzymes were down-regulated or showed no changes in NG samples which were validated by RT-qPCR. Therefore, this study showed that carbohydrate metabolism played important role in seed germination of C.gilva. These findings indicate that C.gilva seeds fail to germinate because they lack energy provision due to the unchanged or down-regulated at the transcriptional level of the key genes and relative enzyme activities in carbohydrate metabolism. The present study will help to understand that the low rate of seed germination existing in C.gilva is at transcriptional and physiological levels.

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