Background

Sucrose is produced in plant leaves following photosynthesis along with other carbohydrates. The key organic compound constitutes the most abundant form of soluble storage carbohydrate, which can be utilized directly by glycolysis or transported from photosynthetic tissues to non-photosynthetic tissues via the phloem [1]. Sucrose therefore serves as a source of fixed carbon that can be distributed systemically throughout the plant, providing fundamental resources for direct energy production or biosynthesis of long chains of biopolymers such as starch [2] and cellulose [3].

Sucrose is synthesized in the cytosol, starting with the export of dihydroxyacetone phosphate and glyceraldehyde phosphate from the chloroplast. The following processes are catalyzed by a series of enzymes [4], in which sucrose phosphate synthase (SPS) is one of the most important ones. SPS catalyzes the conversion of Fructose-6-Phosphate (F-6-P) and UDP-Glucose (UDP-G) to Sucrose-6-Phosphate (S-6-P), providing the substrates for sucrose phosphate phosphatase (SPP). In the final step, sucrose is generated through the removal of the phosphate group. In addition to the well-recognized role of SPS in sucrose biosynthesis in source leaves, it is becoming clear that SPS also plays an important and key role in heterotrophic cells engaging in the net sucrose degradation [5]. For example, significant turnover of the endogenous sucrose pool was observed in germinating Ricinus cotyledons [6]. This turnover of sucrose is thought to be involved in a futile cycle of simultaneous synthesis and cleavage, resulting from changes in the activation rate of SPS phosphorylation [7]. Therefore, SPS plays a crucial role in carbohydrate metabolism by regulating the partitioning of carbon between starch production and carbohydrate (sucrose) accumulation in many physiological and developmental processes.

The role of SPS was first demonstrated in wheat germ extracted by Leloir and Cardini [8] and some plants had multiple SPS genes and expression of these copies varies with developmental stages, tissue types and environmental signals [9,10,11,12], suggesting that SPS genes played divergent roles under different conditions. Recent studies showed that most SPS genes were clustered into three distinct families (A, B and C) and these genes appear to have different evolutionary histories in dicots (A family) and monocots (B family) [4]. Even though one of the SPS isoforms from sugarcane and its closely related partial sequence from barley were grouped in a family, they were somewhat more divergent than the remaining dicot SPSs that have been characterized [4]. SPS transformation experiments indicated that SPS was a major determinant inpartitioning fixed carbon from photosynthesis in the leaf and in the whole plant [13, 14]. Recently, Mark simultaneously increased SPS and glutamine synthetase (GS) activities in transgenic tobacco and found that sucrose was the major determinant of growth and development [15].

Sugarcane is the most important sugar crop in the world since it accounts for 80% worldwide sugar yield [16]. Previous SPS studies in sugarcane showed that the SPS activity and transcript expression showed higher in mature internodes than in immature internodes for all studied cultivars [17]. Meanwhile, compared to the low sugar species of sugarcane, the high sugar species showed increased transcript expressions and enzyme activities of SPS at all developmental stages [17]. In addition, expression of SPS decreased significantly in the late maturing sugarcane variety BO91, compared to the early maturing sugarcane variety CoJ64 [18]. The SPS members were predicted based on public EST databases [19, 20], and DNA fragments of amplified products from Q165 and IJ76–514 cultivars have been characterized to allow the identification all possible alleles [19, 20]. Recent research showed that the N-terminal region of sugarcane SPS played an important role in allosteric regulation [21]. Despite the profound and well-documented role of SPS in sugarcane, the genomic sequences and biological functions for SPS members have not been identified in sugarcane due to their complicated genomes. S. officinarum and S. spontaneum are two of the most important Saccharum species not only because they are the major contributors to the genomes of modern sugarcane varieties, but they are also quite divergent with respect to sugar production [22]. In this study, to comprehensively characterize the SPS family at the molecular and evolutionary level as well as the possible functions of the SPS family in the two main Saccharum species, we analyzed the SPS gene family in S. officinarum and S. spontaneum and illustrated their evolutionary history, the structural and expressional differences as well possible regulatory factors through the utilization of the combinatorial analysis of transcriptome, metabolome and proteome data.

Results

Identification of SPS gene family in S. officinarum and S. spontaneum

LA Purple (S. officinarum, 2n = 80) and AP85–441 (the haploid clone of SES208, 2n = 4x = 32) derived from the anther culture of SES208 (S. spontaneum, 2n = 64) [23] representing two major Saccharum species were used for the construction of the BAC library. Eight and six SPS-containing BACs were isolated from S. officinarum and S. spontaneum, respectively (Table 1), with an average length of 68.6 kb, and maximum length of 127.2 kb (BAC id: SES23E05). TE annotation suggested that Long Terminal Repeats (LTRs) were the major repetitive sequences in most of isolated BACs (Table 1). The putative genes including 14 sugarcane SPS sequences were annotated from the selected BACs (Additional file 1). Among the 14 SPS genes, 11 contained complete ORFs (open reading frames), with the length of coding sequences ranging from 1404 bp to 3321 bp. For further validation, these putative SPS genes were blasted against Sorghum SPS genes and they showed high similarity, with identities ranging from 91 to 100% at the amino acid level (Table 2). In this study, we refer to the sugarcane SPS genes using SPSA to SPSD according to the sequence similarity with Sorghum SbSPSs with a prefix ‘So’ for S. officinarum and ‘Ss’ for S. spontaneum. We also identified SPS genes from the recently published Saccharum spontanenum genome [24]. Five genes without alleles were found with a similarity above 91% compared to 6 BAC sequences (Additional file 2). There were three genes of SPSD2 (Sspon.004A0021251, Sspon.004A0021261 and Sspon.004A0021270) in the S. spontaneum genome. To identify the duplication of three SPSD2 genes, a MCScanX program was used to the analysis referring to the research of Wang et al. [25]. The results indicated that Sspon.004A0021251 may be the primary gene produced from the whole genome duplications (WGDs), and Sspon.004A0021261 and Sspon.004A0021270 were two genes from the tandem duplication. Furthermore, to strengthen the reliability of the sequences, Sspon.007C0001731, Sspon.004A0021270 and Sspon.004A0021251 were re-annotated (Additional file 3).

Table 1 The results of the repeat sequence annotation for BACs containing SPS
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Table 2 Sequence similarity of SPS gene fragments between Saccharum and Sorghum
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Homologs and allelic haplotype analysis of SPS

To identify SPS homologs and alleles in the selected BACs, analysis of conserved synteny was performed (Additional file 4). We observed five highly similar synteny blocks among S. officinarum, S. spontaneum and Sorghum bicolor, indicating that the two Saccharum species contained 5 SPS gene family members. Comparison within each synteny block across the three species showed high sequence identity at DNA level and conserved gene order (Additional file 4). For instance, three SPSD1-containing BAC contigs (LA110E11, SES32E01 and SES69 K24) were identified in Saccharum, two of which were allelic haplotypes from S. spontaneum and one from S. officinarum. Meanwhile, the orthologous region in S. bicolor was also displayed under the three BAC contigs. These sequences shared similar synteny blocks, suggesting that SES32E01 and SES69 K24 were allelic haplotypes in S. spontaneum. Similar results were observed in SPSA, SPSB and SPSC (Table 1). The SPS orthologs in the two species were highly similar, with sequence identities ranging from 95.6 to 100% under the pairwise comparison (Additional file 5).

To further compare the SPS allelic haplotypes, we compared the exon-intron structures of the 14 SPS sequences in S. officinarum and S. spontaneum (Fig. 1a). The SPS genes were clustered together and the alleles are indicated with a, b or c. As expected, most orthologous/paralogous pairs showed similar exon-intron structure. For instance, three SPSC alleles (SoSPSC.a, SoSPSC.b and SoSPSC.c) were identified in S. officinarum and one allele (SsSPSC.a) was identified in S. spontaneum. Despite the overall similarity in gene structure, frequent divergence was also observed among the orthologs and haplotypes, though these proteins were highly conserved at amino acid level. We observed longer gene length and more exons (11 v.s. 9) in SsSPSA.a compared to SoSPSA.a. Notably, one exon was inserted after the second exon in SsSPSA.a and this gene appeared to possess an additional exon at the end of sequence. In addition, we identified three SPSB sequences (SoSPSB.a, SoSPSB.b and SsSPSB.a) in S. officinarum and S. spontaneum. SoSPSB.a and SsSPSB.a showed highly similar exon-intron structure, while the allelic haplotype SoSPSB.b was quite divergent compared to other SPSBs. SoSPSB.b was shorter in gene length and possessed fewer exons compared with SoSPSB.a and SsSPSB.a. This could be due to allelic variation or more likely resulted from incomplete genome assembly. Similar results were also observed in SPSD1. Exons in SsSPSD1.b tended to be shorter and fewer in number than in SsSPSD1.a and SoSPSD1.a.

Fig. 1
figure 1

Gene structure (a) and multiple alignment analysis (b) of SPS. Ss and So indicate two Saccharum species, including S. spontaneum and S. officinarum, respectively. For Fig. 2b, regions of interest were masked with red rectangles: light regulated phosphoserine (I), putative F-6-P binding site (II), 14–3-3 regulated phosphoserine and UDP-G binding domain (III), the osmotically regulated phosphoserine (IV) and various aspartate-proline pairs (DP motif, V, VI, VII)

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Furthermore, we annotated the transposable elements (TEs) within the introns of the SPS genes (Fig. 1a). Four SPS members with the exception of SPSB were revealed to contain TE. TE insertions existed in the second intron of SPSC and the last intron of SPSD2 in S. officinarum but were absent in S. spontaneum. In addition, a large TE was present in SPSD1 from S. spontaneum, suggesting genomic expansion of SPS genes existed in S. spontaneum.

Multiple alignment analysis

We performed a multiple alignment analysis for the Saccharum SPS genes and regions of interest were marked with red rectangles, including light-regulated phosphoserine, putative F-6-P binding site, 14–3-3 regulated phosphoserine and UDP-Glu binding domain, osmotically regulated phosphoserine and various aspartate-proline pairs (Fig. 1b). High sequence similarity was observed in the middle part of SPS proteins. As expected, the F-6-P binding sites and UDP-Glu binding domains are highly conserved at the amino acid level (Fig. 1b, II and III) in most sugarcane SPS proteins. Similarly, the osmotically regulated phosphoserine (IV) and various aspartate-proline pairs (V, VI and VII) are conserved as well, suggesting that these regions play important roles in sugar production. We also observed some mutations that may differentiate the functions of SPS proteins. For instance, a couple of mutations in the SsSPSA F-6-P binding domain (II) likely modified its F-6-P binding activity. A conversion from Serine (S) to Leucine (L) in SPSD family possibly influenced its function in UDP Glu binding (III). Remarkably, light regulated phosphoserines (I) were highly divergent, suggesting that the SPS genes played different roles in response to light regulation. We further investigated the cis-elements in the promoters of the 14 SPS genes (Additional file 6). Cis-elements that are related to the circadian clock, such as circadian and E-box, were observed in most of Saccharum SPSs. In addition, cis-elements involved in abiotic stress were predicted in the promoter region. For instance, ABREs (ABA-responsive elements) were identified in 6 SPS promoters and MYB-binding sites (MBSs) were found in 11 SPS promoters. These results suggested that Saccharum SPS genes might be regulated by the circadian clock and abiotic stress.

Phylogenetic and evolutionary analysis of SPS genes in plants

To further investigate the evolutionary history of sugarcane SPS genes, we first analyzed SPS genes from two dicotyledonous plants (including Arabidopsis thaliana and Vitis vinifera), five monocotyledonous plants (including Ananas comosus, S. bicolor, Brachypodium distachyon, Zea mays and Oryza sativa) and a sole surviving sister species of all other living flowering plants (Amborella trichopoda) (Fig. 2a). All the Saccharum SPS genes identified in this study including alleles were included in the phylogenetic analysis (Fig. 2b). The result showed that selected plant SPSs were clustered into 4 classes (SPSA, SPSB, SPSC and SPSD). Similar to a previous study [4], SPSA, SPSB and SPSC sub-families are present in both monocotyledonous and dicotyledonous plants; while the SPSD gene family only exists in monocotyledonous plants. This result indicated that SPSD genes emerged more recently after the monocot-dicot divergence. In addition, the SPSD gene family had a closer phylogenetic relationship with SPSA than SPSB and SPSC. In A. trichopoda, we only identified two SPS genes (SPSA and SPSC) (Fig. 2a).

Fig. 2
figure 2

Evolutionary analysis of SPS family. a The distribution of SPS subfamilies in different plants. b Phylogenetic analysis of SPS gene family in different plants including S. bicolor, S. officinarum, S. spontaneum, Zea mays, Oryza sativa, Arabidopsis thaliana, Vitis vinifera, Brachypodium distachyon, Ananas comosus and Amborella trichopoda. c Ka/Ks values of the SPS subfamilies

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The non-synonymous to synonymous substitution rate (Ka/Ks) is an indication of selective pressures. A Ka/Ks ratio < 1 is consistent with a history of negative selection, while Ka/Ks ratio > 1 indicates a strong positive selection [26]. We performed a pairwise comparison within each SPS gene from selected plants (Fig. 2c). Almost all SPS genes showed that Ka/Ks values were lower than 1, suggesting that these family members were under strong purifying selection. To further identify the evolutionary forces acting on the sugarcane SPS genes after the divergence of S. officinarum and S. spontaneum, we investigated the Ka/Ks values of SPS genes in S. bicolor, S. officinarum and S. spontaneum (Additional file 7).

Expression profiles of SPS genes in the different tissues of three developmental stages

To investigate the expression pattern of SPS gene families, two transcriptome databases (See materials and methods for details) were used for the expressional analysis from two Saccharum species (S. spontaneum and S. officinarum).

The expression of SPS genes at three different developmental stages (seedling, pre-mature stage and mature stage) were clustered into two trends, demonstrating the significant expressional preference in the stem or leaves of the two species (Fig. 3). One trend is that the genes were much more highly expressed in leaves opposed to the stem, including SPSB and SPSC genes at these three developmental stages, which was consistent with a previous study [27]. Furthermore, SPSB expression was higher than SPSC, indicating that SPSB was the dominant gene expressed in the leaves and functioned in the green tissues of the two Saccharum species. The other trend is that the genes were expressed at significantly higher levels in the stem compared to those in leaves at all developmental stages, in particularly SPSA, SPSD1 and SPSD2 in S. officinarum and S. spontaneum. Similarly, SPSA was the major gene specifically expressed in the stem, especially at the mature stage, which is similar to a previous study that one sugarcane SPS gene was expressed in internodes [17]. Meanwhile, SPSA gene was the highest expressed of all SPS genes and higher in S. officinarum than in S. spontaneum, suggesting SPSA may play an important role in the transportation and storage of sugarcane sugar.

Fig. 3
figure 3

Expression profiles of SPS genes in various samples in two Saccharum species. Seedling, preM and M represent three developmental states: seedling, pre-mature and mature. In each stage, samples include leaves and internodes

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Interestingly, SPSB expression level was higher in the leaves of S. officinarum compared with S. spontaneum at the pre-mature stage, while more SPSA transcripts accumulated in the internodes of S. officinarum at the mature stage than S. spontaneum (Fig. 3), suggesting the differential expressions of SPSA and SPSB may contribute to the differences in sugar yields between S. officinarum and S. spontaneum.

Expression profiles of SPS genes in the segments of developmental gradients in leaves

Based on the above findings, the present study further analyzed the SPS expression patterns in the segments of development gradients in leaves and discovered that certain expressional patterns in almost all genes in these two species (Fig. 4a). We found that SPS gene expressions in S. spontaneum were consistent with the continuous developmental gradient in leaves, while there were three small peaks in S. officinarum (Fig. 4b). In additional, the higher specific expression of SPSB in the leaves of the two species were demonstrated (Fig. 4).

Fig. 4
figure 4

Expression profiles of SPS genes in gradient developmental leaves in two Saccharum species. Leaf samples in the seedling stage were divided into four gradient intervals (a basal zone, sink tissue; a transitional zone, going through the sink-source transition; a maturing zone and a mature zone, active C4 photosynthetic zones), including 15 segments (sec1 to sec15). a The heat map showing the expression levels of all SPS genes. s. b Comparative analysis of the trends in expression of the two species in gradient developmental leaves

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Interestingly, the SPSC expression level was lower than SPSB although the expression patterns were similar, indicating that the functions of SPSB and SPSC were complementary. Furthermore, this study also found that the SPSA, SPSC and SPSD2 genes in S. spontaneum had higher than those in S. officinarum, relatively (Fig. 4a).

SPS protein levels in the two Saccharum species

To further explore the SPS protein levels in sugar synthesis source tissue (leaf) and sucrose accumulation sink tissue (stem), we performed proteomics analysis on a gradient in developing leaves and mature stem tissues (Fig. 5). In leaves, the relative protein content (log2 the intensity of protein) of SPSB was highest among the gene families, which was consistent with transcriptome data. The content of SPSD2 was much higher than that of SPSB, but only slightly higher than SPSC. The protein content of SPS only presented very limited variation between the two Saccharum species, except for SPSD1. The protein contents of the SPS gene families in stem were much different from those in leaf tissue, supporting the theory that SPSs are involved in the distribution of sugar. Importantly, in stem tissue SPSC and SPSD2 presented protein levels similar to leaf tissue, while, SPSB and SPSD1 were much lower than in leaf. These results suggested that the SPSB and SPSD1 contributed to the synthesis of sugar rather than SPSC and SPSD2. In addition, SPSA protein was undetectable in the examined tissues of these two Saccharum species, indicating the limited contribution of SPSA.

Fig. 5
figure 5

The relatively content of SPS protein in gradient developmental leaves (a) and stems (b) in two Saccharum species. The developmental leaves and stems were all divided into three developmental blocks: basal-zone (basal-z), maturing-zone (maturing-z), mature-zone (mature-zone)

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Sugar contents in the two Saccharum species

To further analyze the SPS functions in sugarcane, metabolomics analysis was carried out and the changes in small molecule concentrations that were closely related to the phenotype of sucrose trait in two Saccharum species were examined. Based on the two sets of metabolome data from leaves at seedling and stems at mature stage, there were two metabolites, which were directly associated with SPS enzymes, F-6-P (a substrate during SPS catalyzing the synthesis of sucrose) and sucrose (end products of SPS catalytic pathway).

For the gradient of developing leaf, an increasing trend of sucrose content was found in S. officinarum and S. spontaneum as the leaves became into mature (Fig. 6a), while a generally decreasing trend of F-6-P content was found in the two species, indicating SPS performed the same functions during the sucrose synthesis in the two varieties. For the stems at different developmental states, sucrose content showed different tendencies among three species, such as a significant increase in S. officinarum and increasing from young stems to the maturing zone and then declining from the maturing zone to the mature zone in S. spontaneum (Fig. 6b).The levels of F-6-P were almost not detectable in three stem nodes from both S. officinarum and S. spontaneum. Whereas the sucrose contents in the seedling leaf and the stems were higher in S. officinarum than in S. spontaneum.

Fig. 6
figure 6

Metabolomics analysis in gradient developmental leaves (a) and stems (b) in two Saccharum species. The sampling methods are consistent with the proteomics analysis. The comparative analysis of the metabolites of sucrose and fructose-6-phosphatein in leaves and stem internodes of two species were performed, Fructose − 6-phosphate was not detected in the stems of either species

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Discussion

Gene evolution of SPS family

In this study, we used an extensive collection of plant SPS gene members for phylogenetic analysis, and the results confirmed the previous classification [4], and revealed the existence of three groups (SPSA/B/C) containing both monocotyledonous and dicotyledonous and one Poaceae species specific SPSD (Fig. 1b). A. trichopoda only contained two SPS genes (AtrSPSA and AtrSPSC), suggesting that SPSB newly evolved after the divergence of A. trichopoda and other flowering plants (Fig. 2a). The evolutionary history of Saccharum SPS, which was sorted chronologically, from ancient to more recent, was SPSA/SPSC, SPSB, SPSD1/SPSD2.

Previous studies showed that SPSD genes emerged after the monocot-dicot divergence [28]. In our study, phylogenetic analysis more precisely showed that the SPSD genes likely evolved from the SPSA clade (Fig. 2b) and were clustered into two groups, SPSD1 and SPSD2.Interestingly, SPSD genes only existed in Poaceae plants rather than in A. comosus, which was an out group to the Poaceae lineage in the Poales order [29], suggesting that SPSD genes evolved separately within the Poaceae clade. Within the coding regions, the Ka/Ks ratios of SPSD genes were much less than 1, indicating that purifying selection was the dominant force driving the evolution of SPSD genes after the speciation of the two Saccharum species. Molecular evolution analysis revealed that negative selection drove the evolution of these genes, suggesting their functional importance.

Sorghum is the closest relative in the diploid genera of Saccharum. Comparative analysis of the orthologs between Sorghum and Saccharum made it possible to investigate the specific evolutionary events after the polyploidization of Saccharum. Previous studies indicated that polyploidy was a powerful player in the acceleration of evolutionary adaptation [30]. One research group detected the rapid spread of beneficial mutations in tetraploid yeast in response to growth on a poor carbon source [31]. To investigate the evolutionary pressure of SPS genes in polyploidy Saccharum, we calculated the Ka/Ks ratios in S. bicolor, S. officinarum and S. spontaneum (Additional file 7). Almost all the pairwise comparisons of Ka/Ks that were statistically significant were lower than 1 (Fisher test, p < 0.05), indicating that SPS genes in Sorghum and Saccharum were undergoing purifying selection. However, the Ka/Ks ratios within Saccharum were significantly higher than between Sorghum and Saccharum (mean = 0.22 versus mean = 0.12, Mann-Whitney-Wilcoxon test, p = 0.01775). This result showed that polyploidy relaxed selection pressure of SPS genes in Saccharum, which is an expected outcome of gene redundancy in the polyploids.

TEs influence the evolution of SPS genes

TE are a dominant feature of most flowering plant genomes and greatly contribute to genome evolution and diversity [32]. TEs are known to have a cis-effect on gene regulation since the likely DNA and histone methylation are often associated with the repetitive sequences. A previous study showed that high levels of TE variation was observed between S. officinarum and S. spontaneum [33]. In our research, comparison of SPS genes between the two sugarcane species displayed frequent TE variation, indicating that TEs could play important roles in SPS evolution before and after the divergence of S. officinarum and S. spontaneum. For instance, two adjacent TEs were inserted into the seventh exon of SoSPSC.a, SoSPSC.b and SsSPSC.a, suggesting that the events happened before the divergence of the two sugarcane species (Fig. 2a). However, another TE insertion event was only observed in the second intron of SoSPSC.a and SoSPSC.b rather than SsSPSC.a, suggesting that SPSCs were undergoing independent evolution after divergence of the two Saccharum species.

SPSD genes were unique in monocots and our results uncovered that they were likely undergoing quite different evolution fate. SoSPSD2.a and SsSPSD2.a were conserved in exon-intron order, gene length as well as TE distribution (Fig. 2a). However, it seemed that gene structure of SPSD1 was less conserved and the last exon of SsSPSD1.a was interrupted by a large TE insertion. Moreover, the SsSPSD1.b displayed a quite different exon-intron structure as well as a large TE variation compared to its allelic haplotype SsSPSD1.a. These results demonstrated that the two copies of newly evolved SPS subfamily tended to be functional diverse in sugarcane and possibly TE variation was responsible for the diversity, providing the first evidence that TE contributed to gene evolution in Saccharum.

Functional divergence of SPS genes between S. officinarum and S. spontaneum

RNA-seq analysis revealed that the expression of SPSB in S. officinarum leaves were higher than that in S. spontaneum leaves at the pre-mature stage (Fig. 3). Furthermore, the expression of SPS in leaves displayed consistent trend between these two Saccharum species as a developmental gradient progressed (Fig. 4), indicating that the expression of SPS was responded to photosynthesis in the seedling leaf.

SPSB presented consistently high expression in S. officinarum which was consistent with the highest relative content of SPSB protein based on proteomics analysis, indicating SPSB may be the major gene of sucrose synthesis in leaf. Combined with metabolome data, we found in leaves at the seedling stage that sucrose increases and F-6-P decreases, which was in line with the trend of expression of SPS (especially the SPSB gene with high expression level) mentioned above which catalyzes the synthesis of sucrose using substrate of F-6-P. Therefore, in the source tissue leaves, we speculate that SPS genes play a key role in sucrose synthesis which is linked with the development of leaves in these two Saccharum species. Further correlation analysis revealed there were significant overall relationships between the expression of SPSB and the content of sucrose (positive, r2 = 0.97 or 0.93) or F-6-P (negative, r2 = 0.84 or 0.90) both in S. officinarum and in S. spontaneum (Additional file 8). Since the characteristics of high and low sugar in cultivated sugarcane were respectively contributed by S. officinarum and S. spontaneum, the gene expression of SPSB may play a significant role in sucrose synthesis and accumulation. These results revealed that SPSB played a vital role in sugarcane leaves and they might be responsible for the difference of sugar at the production process of growth. However, considering the limited data points for correlation analysis, further verification of experimental data is required to support this speculation.

Besides SPSB, SPSA also showed different expression patterns at seedling and mature stages between the two species (Fig. 3). SPSA was expressed at higher levels in S. officinarum stems than in S. spontaneum stems, which may be caused by different gene structures given that there are 9 exons in SoSPSA and 11 exons in SsSPSA (Fig. 1a). Interestingly, when the expression of SPSA in different segments of the developing stem and metabolites were compared, a significant nonlinear negative relationship did exist among three species, except for one special case in S. spontaneum in which the sucrose content was to some extent correlated with the expression of SPSA (r2 = 0.84 or 0.90) in the stem (Additional file 8). This indicates that there could be other genes regulating sucrose synthesis in stem tissue or other influencing factors, resulting in differences in sucrose content in stems of the three species. Considering the lower expression of SPS genes in stem tissue than in leaf tissue and the higher expression levels of SPS genes in S. officinarum than in S. spontaneum, lower levels of sucrose synthesis in stems and high sucrose content in stem were mainly obtained through sugar transport rather than sugar synthesis.

The results revealed that SPSD duplicated within the Poaceae lineage. The gene expression data revealed that the two SPSDs had similar expression patterns, suggesting functional similarity in this gene subfamily. Furthermore, SPSD maintained similar lower expression levels in all tissues of sugarcane and there are no specific expressive features, which is different from wheat SPS genes but consistent with the research in sugarcane [34]. However, both SPSD gene copies were under strong purifying selection shown by their Ka/Ks ratio < 0.4 (Additional file 7), which demonstrated that the two SPSDs were not functionally redundant in Saccharum. Therefore, we assumed that two SPSD subfamilies shared similar gene functions while possessing divergent key functional roles in Saccharum. Proteome data showed that the protein content of SPSD2 was highest in the developing stem and SPSA protein was not detected in leaves and stem internodes. Combined with the recent replication events in which SPSD evolved from SPSA and the high levels of SPSA gene expression in the stem, we suspected that the high SPSA expression levels in stem tissue regulates the translation of the highly homologous genes of SPSD, which lead to the high protein levels of SPSD2 and free protein content of SPSA in stem tissue in both two species.

Interestingly, the present study found SPS expression was affected by photosynthesis and regulated by circadian rhythm. All SPS in the gradient of developing leaves displayed differing expression patterns, indicating SPS expression was synchronized with light assimilation in a species-specific manner (Fig. 4). Therefore, we assumed that the SPS contributed to the differential sugar accumulation between the two Saccharum species. Notably, SPS expression was sometimes regulated by other enzymes, for example, SPS had potential functions and contributed to sucrose synthesis when soluble acid invertase (SAI) was low [35].

Meanwhile, based on the high spatiotemporal specificity of SPSB and SPSA in the SPS gene family and the simultaneous comparative analysis of transcriptomes, metabolomes and proteomes, we suspect that the regulation of SPSB and SPSA gene expression are an important point in the formation of regulatory networks in sucrose traits among different species of Saccharum. Furthermore, sucrose accumulation is a complex process requiring the involvement of multiple genes for sugar synthesis, transport and the targeting regulation. In this way, different patterns of expression of SPS genes may be formed among different sugarcane species, leading to different levels of photosynthetic carbon fixation and even different sugar traits.

Conclusions

We presented a comprehensive analysis of SPS genes in two Saccharum species, including S. spontaneum and S. officinarum. By analyzing and comparing these SPS genes, we concluded three major findings. Firstly, frequent structural variations and mutations were observed in SPS homologs as well as allelic haplotypes. In addition, comparison of SPS genes between the two Saccharum species displayed several TE insertion/deletion events, indicating that TEs might play important roles in SPS evolution before and after the divergence of the two species. Secondly, the SPSD subfamily in Saccharum was newly evolved from SPSA after the generation of Poaceae species. Molecular evolution analysis showed that all the SPS genes were under negative selection and selection pressure was reduced under the process of polyploidy. Thirdly, RNA-seq, metabolome and proteome data uncovered the different expression patterns of the SPS gene family between S. officinarum and S. spontaneum, suggesting that SPSA and SPSB are possibly responsible for the fundamental differences in sugar yields at different stages. The high expression of SPSB in developing leaves may play a direct and key role in sucrose synthesis at the source organization, and the accumulation of high sucrose levels In the stem there may be a more complex regulatory network in which SPSA and SPSD genes are indirectly involved.

Methods

Plant materials and RNA extraction

Sugarcane species LA-Purple [36, 37] (S. officinarum, 2n = 8x = 80, originated in USA) and SES208 [38] (S. spontaneum, 2n = 8x = 64, originated in USA) [39, 40] that were deposited in the National Germplasm Repository of Sugarcane (Yunnan, China) and used in the present study. Plants were grown in plastic pots under greenhouse conditions (14:10 L/D, 30 °C L/22 °C D and 60% relative humidity) and standard growing practices in the sugarcane experiment field at Fujian Agricultural and Forestry University (Fuzhou, China). For the investigation of the different developmental stages and the gradient developmental leaf analysis experiments were performed as previously described [41]. Tissue of developmental leaf was collected 3 h into the L period as detailed by Li et al. [42] and the collection of other tissues was detailed by Ming et al. [29]

RNA from various sugarcane tissue samples were extracted using Trizol (Invitrogen, USA) according to the manufacturer’s protocol. RNA was digested with DNase I (NEB, USA) and the RNA integrity was assessed using the Agilent Bio-analyzer 2100 system (Agilent Technologies, CA, USA).

BAC libraries

A list of BAC libraries were constructed from the haploid genome of S. spontaneum (AP85–441, 2n = 4x = 32) and S. officinarum (LA Purple, 2n = 80). Nuclei were isolated from young leaf tissues following the protocol in [43]. Briefly, high molecular weight DNA was extracted and digested into fragments with HindIII. Approximately 100 kb fragments were isolated and inserted into the pSMART BAC vector (Lucigene, LA). BAC clones which contained potential SPS genes were sequenced using an Illumina Hiseq 2500. There were 74,880 clones in BAC library of LA Purple based on 195–384 well plates with 150 kb of an average insert size, which provided the 1.5 x and 12 x coverage of the octoploid genome and the monoploid genome, respectively. For AP85–441, the BAC library was consisted of 38,400 clones (in the 100–384 well plates with an average insert size at 120 kb), which was an about 1.5 x coverage of the haploid (tetraploid) genome and an about 6 x coverage of the monoploid genome.

Assembly, annotation of BAC reads and identification of SPS gene family

The raw reads were assembled using SPAdes 3.6.2 with default parameters [44]. Firstly, we designed the probes using Sorghum SPS sequences (Additional file 9) and matched the probes against sugarcane BAC libraries. We then performed De novo assembly and gene annotation for the selected BACs. To get a better annotation, TEs and tandem repeat sequences were identified. The assembled contigs were screened using RepeatMasker [45] for TE annotation and to the DNA subway system (http://dnasubway.iplantcollaborative.org/) for coding region annotation using the model of “Annotate a genomic sequence” referring to the tips on the web page (https://github.com/CyVerse-learning-materials/dnasubway_guide). For TE identification, annotation was confirmed if there was at least 60% identity and a minimum length of 50 bp with previously annotated plant TEs (ftp://ftp.plantbiology.msu.edu/pub/data/TIGR_Plant_Repeats/).The corresponding coding sequences were translated into protein by the EXPASy-translate tool (http://web.expasy.org/translate/).

SPS homologs from other plants, such as Arabidopsis, maize and so on, were downloaded from NCBI (http://www.ncbi.nlm.nih.gov/). A blast-based search was performed to identify SPS gene family from S. officinarum and S. spontaneum, as well as selected plants listed in Additional file 5. The cutoff was set as E-value less than 10− 20 and identity higher than 85%.

Gene structure, phylogenetic and evolutionary analysis

The exon-intron structures were extracted from gene annotation data generated by DNA subway and further displayed in GSDS (Gene Structure Display Server) [46]. In addition, MultAlin [47] program was used to illustrate the multiple sequence alignment of amino acids and domains of interest for sugarcane SPS genes. Plant CARE (http://bioinformatics.psb.ugent.be/webtools/plantcare/html/) was used to predict cis-elements in SPS promoters.

To generate a phylogenetic tree, the predicted protein sequences from seven plants, including two Saccharum species (S. officinarum and S. spontaneum), Zea mays, Sorghum bicolor, Arabidopsis thaliana, Brachypodium distachyon, Vitis vinifera, Ananas comosus, Oryza sativa and Amborella trichopoda were initially aligned based on Clustal W2.0 [48]. Then we reconstructed the phylogenetic tree of SPS genes by the Neighbor-Joining method estimated by the JTT amino acid matrix implemented using the program MEGA 4.0 [49]. The pairwise deletion option was set in the NJ tree reconstruction and the accuracy of the tree topology was assessed by bootstrap analysis with 1000 resampling replicates.

Genes of SPSA, SPSB, SPSC and SPSD were initially aligned using Clustal W2.0 [48]. The aligned sequences were subject to KaKs_Calculator [50] to calculate the Ka and Ks values.

Expression analysis using RNA-seq data

For two blocks of expression profile, the first batch of samples were from different tissues at three different developmental stages (seedling, pre-mature stage and mature stage), including 2 leaves (mature leaf and leaf roll) and 3 stalks (mature, maturing and immature stalk). In additional, the second batch of samples were segments of the gradient developmental leaf, which was divided into 15 clips in four zones: a basal zone (base, sink tissue), a transitional zone (going through the sink-source transition), a maturing zone and a mature zone (active C4 photosynthetic zones, fully differentiated).

Paired-end sequencing (100 bp reads length) was performed using the HiSeq 2500 platform. The raw data was obtained from a list of RNA-seq libraries and initially filtered using Trimommatic [51] with default parameters. Then Tophat program was performed to map clean reads to assemble BAC contigs and the FPKM values for each SPS gene were calculated using cufflinks [52].

Metabolomics experiments

Leaves in seedling and stems in the mature stage were sampled from two species, LA-Purple (S. officinarum), SES 208 (S. spontaneum), respectively. For the leaf samples, we selected three segments during different developmental stages, 0–2 segments (basal zone), 7–9 segments (maturing zone) and 13–15 segments (mature zone). For stem samples, the internodes selected were the same as in the experiments involving RNA-seq, three zones (basal zone, maturing zone, mature zone), stem 3, 6, 9 in S. spontaneum and stem 3,9,15 in S. officinarum. Samples were harvested and ground with a ball mill precooled with liquid nitrogen, brought back to extract and derivatize the metabolites. Chromatographic analyses of derivatized samples were achievedusing a random injection sequence, on an Agilent 7890B GC (Agilent, Atlanta, GA, USA), coupled to a Pegasus HT time-of-flight mass spectrometer (LECO, St. Joseph, MI, USA), equipped with a Gerstel MultiPurpose Sampler (MPS) (Gerstel, Mülheim an der Ruhr, Germany). Five experimental replicates of each tissue were tested. Raw data was processed using ChromaTOF (version 4.51.6, LECO, St. Joseph, MI, USA) to identify the metabolites. Signal redundancy per metabolite was manually corrected and based on the normalized mean area of selected ions, quantitative measurements of analytes were performed after normalization using the IS. Next, by matching the mass spectra with those of authentic standards, public and commercial databases (NIST, Fiehn and Golm metabolome databases), the metabolites were identified. Chemical standards were purchased from Sigma (St. Louis, MO, USA) or Fluka (Milwaukee, WI, USA). For comparing the abundances of metabolites, the data matrix consisting of mass features and peak area values were exported from ChromaTOF to Excel. The mean peak area abundance values from five technical replicates were calculated after normalization to IS [53].

Proteomics experiments

To investigate the proteomic changes among two sugarcane species, the leave and stem samples analyzed were of the same stage as those used for the metabolomics experiments. To prepare the protein, one gram of fresh tissues was collected and the experimental method was referred to the FASP procedure [54] and the manufacturer’s instructions (Thermo Fisher Scientific) following the TMT labeling procedure. We then prepared the sample to be fractionated by the strong cation exchange (SCX) chromatography as previously described [55]. On a nanoflow HPLC (Proxeon Biosystems, now Thermo Fisher Scientific), we performed RP-HPLC separation. The resolution for MS/MS spectra was set to 17,500 at m/z 200 and the normalized collision energy was 29%. Reporter ion quantitation was based on the extraction of the TMT reporter ion signals of each peptide by MaxQuant software. The contents of proteins with the value of peak area were then quantified by summing reporter ion counts across all peptide matches, and then normalized by assuming equal protein loading across all samples.