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. 2019 Sep 28;9(10):376. doi: 10.1007/s13205-019-1908-0

Transcriptome analysis of the effect of GA3 in sugarcane culm

A Chandra 1,✉, K Roopendra 1, I Verma 1
PMCID: PMC6765626  PMID: 31588400

Abstract

Our earlier studies have indicated that GA3, being a growth hormone, increases internodal length, in turn increasing sink strength and improving sucrose accumulation in sugarcane. In this study, transcriptomic level analysis was carried out on internodal samples of a high sugar accumulating variety (CoLk 94184) of sugarcane, to determine the effect of exogenous application of GA3 vis a vis functional analysis of differentially expressing transcripts. Overall, a total of 201,184 transcripts were identified, with median contig length of 450 bp and N50 length of 1029 bp. Analyzing the data from control and GA3-treated canes, at 0.01 significance, a total of 1516 differentially expressing transcripts were identified in bottom internodes and 1589 in top internodes. A KEGG (enrichment) analysis grouped the transcripts into 153 plant-related functional categories. From among these, the transcripts which were functionally relevant to sugar metabolism and photosynthesis were sieved out. Starch and sucrose metabolizing genes showed maximum fold change of 5.0 and 3.0 among top and bottom internodal samples. A homology match using Blastx analysis tool yielded 65 transcripts/differentially expressed genes (DEGs) which were found to share homology with C4 plants like Saccharum, Sorghum and Zea mays. Differentially expressing transcripts from both top and bottom internodes were validated by qRT-PCR, indicating their importance in such study. Results also enriched sugarcane transcriptome resources useful for omics study in genus Saccharum and family Poaceae.

Electronic supplementary material

The online version of this article (10.1007/s13205-019-1908-0) contains supplementary material, which is available to authorized users.

Keywords: Sugarcane, Transcriptome, Gene expression, Gibberellins, RNA-Seq

Introduction

Sugarcane is an important industrial crop, grown in both tropical and subtropical regions, accounting for 80% of the raw sugar produced worldwide (Zhang et al. 2018). As a result of a series of inter-specific crosses with wild species, present day sugarcane is an aneuploid allopolyploid (Premachandran et al. 2011). The complex genetic makeup poses difficulty in improving sugarcane by conventional breeding (Dunckelman and Legendre 1982). The estimated polyploid genome size of sugarcane ranges from 3.36 to 12.64 Gb, and the monoploid genome size ranges from 800 to 900 Mb (D’Hont et al. 1996, 1998; Zhang et al. 2012; Garsmeur et al. 2018). Though in recent past a mosaic monoploid reference sequence of sugarcane and allele-defined genome of the autopolyploid sugarcane S. spontaneum has been reported (Garsmeur et al. 2018; Zhang et al. 2018), researchers worldwide have resorted to transcriptome study to derive plethora of genes and carry out functional analysis of those associated with various agronomical traits, especially when complete reference genome sequence is not available, as in case of sugarcane. Till date, transcriptome level endeavors on sugarcane have mostly derived data from Saccharum officinarum gene indices (SoGI), de novo assembled transcript contigs from short reads as well as genes associated with leaf abscission and water stress (Li et al. 2016; Belesini et al. 2017; Hoang et al. 2017). Also, an expressed sequence tag (EST) project has been reported to provide a few hundred “single-pass” sequences of anonymous genes (Carson and Botha 2000). Though sugarcane is a polyploid, GenBank release of early 2017 indicated a total of only 373,971 DNA sequence entries of all types, derived from various species of the Saccharum genus, thus indicating slow listing of such DNA sequences in GeneBank, perhaps owing to the complexity of the crop per se.

Latest well-equipped DNA sequencing technologies viz. Next-generation DNA sequencing (NGS) have boosted chances to unravel and understand complex crop genomes (Duran et al. 2010; Edwards and Batley 2010; Varshney et al. 2009). Also, a number of bioinformatic tools available help in dealing with the large amount of data generated by its bioinformatic analysis and annotation (Lai et al. 2012; Lee et al. 2012; Marshall et al. 2010). Thus, transcriptome sequencing is an efficient method for analysis and evaluation of expression patterns in large, complex genomes and providing functional annotation therein, of genes underlying key agronomic traits and allelic variation of interest to breeders.

Due to the complex background and high costs involved, whole genomic sequencing of sugarcane remains a daunting task. However, high-throughput sequencing technologies, such as Roche/454 and Illumina/Solexa, have now devised options for generating transcriptome resources at large scale and relatively low cost (Mardis 2008; Varshney et al. 2009). In a recent attempt, Solexa technology was employed to study sugarcane infected with Sporisorium scitaminea, which brought to light 2015 differentially expressed genes thought to be involved in pathogen response (Wu et al. 2013). De novo assembly by Illumina RNA-seq platform and transcriptome annotation was utilized to generate database of six sugarcane genotypes involved in bi-parental crosses (Cardoso-Silva et al. 2014). Recently, a Chinese high sucrose sugarcane variety namely GT35 was analyzed using Illumina HiSeqTM 2000 pipeline and KEGG pathway analysis of 30,756 unigenes revealed more than 30 pathways in the sugarcane transcriptome (Huang et al. 2016). Also, a total of 14,613 unigenes were assigned gene ontology terms, and 13,231 were assigned functional annotations and grouped into 25 functional categories. India being the second largest sugarcane and sugar producing country in the world, transcriptome-based research to decipher differentially expressing transcripts involved in sugar synthesis, transport and accumulation in sugarcane is still lacking.

Sugarcane is able to partition carbon to sucrose in the stem, called sink, in co-ordination with ‘source’ leaves. Since sugarcane accumulates high level of sucrose in its stalk, it has become an important cash crop and a model plant to study source (leaf)–sink (culm) communication especially by way of identifying differentially expressing transcripts having functional significance. The current commercial cultivars’ sucrose yields (350-400 mgg−1 dry matter) are much lesser than the predicted accumulation capacity of sugarcane culm tissue (500–560 mgg−1 dry matter) (Jackson 2005). This glaring difference points to the scope for harnessing the ‘true’ sucrose accumulation potential. The physiological limit of sucrose accumulation in the culm is perhaps governed by the feedback regulation of sugars. Hence, a detailed understanding of genes involved in sugar metabolism is required, to devise means to uncouple the signaling pathways that mediate negative feedback between source and sink tissues, and improve sucrose yield therein. Studies have shown that exogenously applied gibberellins bring about increase in internodal length by promoting elongation and division of cells (Iqbal et al. 2011; Verma et al. 2017). Our earlier study indicated that gibberellic acid (GA3) has affected the source–sink communication, sucrose metabolism and consequently sucrose accumulation in the sugarcane culm by heightening sink demand (Verma et al. 2017; Roopendra et al. 2018). A transcriptomic level comparison of GA3-treated and untreated data sets, identification and functional analysis of novel genes/transcripts therein can further enhance our knowledge about gene networks that are potentially involved in carbohydrate metabolism, and in regulation of sugar-based feedback signals, that limit photosynthetic rates and in turn affect sucrose concentration of culm, possibly by increasing sink strength and thereby its demand.

In this purview, the current study was carried out to ascertain the effect of GA3 at transcriptomic level. In this study, the transcriptome of the robust, high sugar accumulating variety CoLk 94184, was sequenced and analyzed. To better understand the molecular mechanisms underlying differential responses to gibberellin treatment, transcriptomic changes were evaluated in two data sets viz. bottom and top cane internodes, in control and GA3-treated plants, grown under normal conditions. To the best of our knowledge, this is the first report where an early maturing high-sugar-bearing sugarcane variety (CoLk 94184) of sub-tropical zone of India has been utilized for transcriptome analysis.

Materials and methods

Plant sampling

An early maturing, high sucrose accumulating cultivar (CoLk 94184), planted at Indian Institute of Sugarcane Research farms (26.78°N, 80.99°E, 111 msl) Lucknow, India, in the last week of February, 2014 (using three bud setts), was used for sampling. Solution of gibberellin (viz. GA3) (35 ppm spray) was exogenously applied 90 and 120 DAP (days after planting), to alter sugar metabolism. Sampling was done in the month of September (i.e. 210 days after spraying), using three controls (non-sprayed) and three GA3-sprayed plants, of similar height and girth, grown under normal growth conditions, as biological replicates. Based on number of internodes, each cane was divided into three equal portions, and tissue from specific (top/bottom) internodes of the control and GA3-sprayed canes was used as sample for RNA isolation. Both top and bottom internodes were taken for analysis keeping in view that the sugar formed in leaves is transported through top to bottom portion of canes.

RNA extraction, cDNA library and Illumina sequencing

For isolation of total RNA, stalk tissue from bottom and top portions of control (SCB-1/SCT-2) and treated (SGB-3/SGT-4) canes was used as four experimental samples. One gram fresh tissue from each sample was ground to fine powder using liquid nitrogen. Total RNA was isolated using Trizol reagent (Invitrogen, USA). The RNA quality was checked on 1% agarose gel and RNA was quantified using NanoDrop (Quawell UV–visible spectrophotometer) and an Agilent 2100 Bioanalyzer (Santa Clara, CA, USA). Poly (A) mRNA was enriched from total RNA and paired-end transcriptome sequencing was performed by employing Illumina HiSeq™ 2500 (NxGenBio Life Sciences, New Delhi) to retrieve four sequenced libraries defined as SCB-1, SCT-2, SGB-3 and SGT-4.

De novo transcriptome assembly

After RNA-sequencing, the raw data were purified by trimming adapters and removing low-quality sequencing to get clean reads, brought about by Fast QC. All the clean reads were then assembled by employing a de novo assembly program Trinity (Grabherr et al. 2011) (trinityrnaseq-2.1.0). For each library, the assembly program organizes the short reads with a certain length into longer contiguous sequences (contigs) based on their overlap regions. The contigs from different transcripts were mapped, based on paired-end information to get transcript sequences.

Functional annotation

Functional annotation of all the assembled transcripts was done by aligning the sequences to publicly available databases including gene ontology (GO) (http://wego.genomics.org.cn/cgi-bin/wego/index.pl), and the Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.genome.jp/kegg/kegg2.html). National Center for Biotechnology Information (NCBI) non-redundant protein (Nr) database was employed to carry out homology search of selected transcripts using the BLASTx analysis.

Expression profiling and gene enrichment analysis

The functional annotation pertaining to KEGG was utilized to perform an enrichment study and to narrow down on transcripts that were specifically plant related. The expression level of transcripts from samples across treatment groups was quantified and differential expression was determined using edgeR. The obtained transcripts were quantified to get the number of transcripts pertaining to each sample and hence, absolute expression was determined. The FPKM (fragments per kb per million reads) values were used to determine the expression level of each transcript. This eliminates the influence of different gene lengths and sequencing discrepancies (Mortazavi et al. 2008). Fold change in the expression of each gene, i.e. the ratio of the FPKM values was determined to identify differentially expressed genes (DEGs) among each data set. The false discovery rate (FDR) control method was used to identify the threshold of the P value in multiple tests to compute the significance of the differences in transcript abundance (Benjamini and Yekutieli 2001). Since very large number of transcripts were found to show a fold change of more than two, we employed a fold change with an absolute value of log2 ratio ≥ 5 as the threshold, to identify and fish out transcripts showing significant difference in expression, in turn, bringing down the transcript number. Hence, from among transcripts showing significant differential expression, transcripts exclusively showing high expression in a sample set could be sieved out from the transcripts overlapping between data sets. The log2-transformed FPKM values of the overlapping genes were used to generate heat map by Heml (Deng et al. 2014).

Validation of differentially expressed genes through qRT-PCR analysis

Based on the fold change in expression, transcripts showing up- and down-regulation were selected for their validation using total RNA isolated from top and bottom internodes of control and GA3-sprayed plants. Annotation details of selected transcripts were searched and different families of genes were selected for validation through quantitative real-time PCR (qRT-PCR) (Table S1). First-strand cDNA synthesis was done from 2 µg of total RNA, priming it with oligo-dT and using RevertAid H minus Reverse Transcriptase (Thermo Scientific), as per the manufacturer’s instructions. Primer Premier 6.0 software was used to design the gene-specific primers (Table S1) which were used for quantitative reverse transcriptase PCR (qRT-PCR). The real-time PCR was carried out in 48-well plate in a Step One Real-Time PCR system (Applied Biosystems, USA) using SYBR Green PCR Master mix (Applied Biosystems, USA). The cycling conditions used were as follows: 50 °C for 2 min and 95 °C for 10 min as holding stage, 40 cycles of 95 °C for 15 s and 60 °C for 1 min. The gene encoding 25S rRNA [F-5′-GCAGCCAAGCGTTCATAGC-3′/R-5′-CCTATTGGTGGGTGAACAATCC-3′] (Iskandar et al. 2004) was used as reference (i.e. for calibration) in all reactions. The minimum information for publication of quantitative real-time PCR experiment (MIQE) sheet demonstrated the experimental details of the PCR reactions (Table S2). Real-time PCR was carried out using the relative quantification method and expression ratios were computed from cycle threshold (CT) values to obtain 2−∆∆CT or RQ values, depicting fold change.

Results and discussion

Analysis of transcriptome identified from culm tissues of sugarcane

The total RNA samples derived from bottom and top internodes of control and GA3-treated canes were checked for quantity and quality, and were reported to have concentrations of 118 ng/ul, 38 ng/ul, 42 ng/ul, 86.7 ng/ul with RIN of 7.7, 8.2, 7.6, 7.4, respectively. These data showed that the quality of starting material was good enough to warrant further analysis.

In an earlier study conducted by employing cDNA-SRAP technique, some fragments were found to be different between the GA3-treated and control samples, for different gene transcriptions in the young stem of sugarcane (Wu et al. 2010a, b). To determine the effect of gibberellin treatment at transcriptomic level in sugarcane, four Illumina libraries viz. SCB-1 (control bottom internodes), SCT-2 (control top internodes), SGB-3 (GA3-treated bottom internodes), SGT-4 (GA3-treated top internodes), representing bottom and top internodes of control and GA3-treated canes, respectively, were generated for RNA-sequencing. The four libraries produced a read count of 18,427,294, 11,165,152, 17,975,549 and 18,054,125, respectively, with GC percentage of 49 and 50 each. The raw reads were subjected to quality check using the fast QC tool. Since all reads lay within the permissible range, no raw data trimming was required. Sugarcane lacks a sequenced genome, till date; hence, Trinity was employed to carry out de novo assembly. A total of 201,184 transcripts were identified, with median contig length of 450 bp and an N50 length of 1029 bp. Results have indicated usefulness of RNA-seq analysis in generating large-scale transcriptome datasets even in the absence of reference genome (Feng et al. 2012). Mean length of the transcripts reported here was observed to the same level as reported in Chinese sugarcane variety GT35 (Huang et al. 2016); however, it was shorter than those reported in other sugarcane variety (Cardoso-Silva et al. 2014). However, the N50 length of the transcripts was more than those reported with other sugarcane variety (Huang et al. 2016). These transcripts were assembled into 132,293 genes (Table 1). Overall, a total of 54,596 transcripts were expressed exclusively in control cane samples, with 35,312 in bottom internode sample and 19,284 in top internode. Also, a total of 55,589 transcripts were unique to GA3-treated samples, 15,559 in bottom internode sample and 40,030 in top internode. When transcripts exclusively expressed in bottom internodes were compared, a total of 35,312 transcripts were found to be differentially expressed in control and 15,559 in GA3-treated sample. Similarly in top internodes, 19,284 transcripts expressed in control and 40,030 in GA3 sample (Fig. 1). Results indicated that in top internodes, GA3 treatment induced more transcripts to differentially express as compared to that in bottom internodes.

Table 1.

De novo assembly statistics of two data sets

Character Number
Total genes 132,293
Contigs generated 201,184
Maximum contig length 12,090
Minimum contig length 224
Average contig length 707.34
Median contig length 278.5
Total contig length 142306097
% GC 47.78
Contigs ≥ 100 bp 201,184
Contigs ≥ 500 bp 90,780
Contigs ≥ 1000 bp 42,804
Contigs ≥ 10000 bp 3
Contigs ≥ 1 Mbp 0
N10 2516
N20 1958
N30 1597
N40 1297
N50 1029
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Fig. 1.

Fig. 1

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Venn diagram representing differentially expressed transcripts in bottom sample of control (SCB-1) and GA3-treated (SGB-3) canes (a) and in top sample of control (SCT-2) and GA3-treated (SGT-4) canes (b)

Functional annotation of transcripts and their significance in sugarcane physiology

Normalized gene expression levels were calculated using Cufflinks version 0.9.3 (Trapnell et al. 2010) and are reported as fragments per kilobase pair of exon model per million fragments mapped (FPKM). Considering the parameter of fold change, a total of 108,267 differentially expressed transcripts were obtained from the SCB-1-SGB-3 data set, with 3701 transcripts showing significant (0.1) differential expression. Similarly, 102,109 differentially expressed transcripts were retrieved for the SCT-2-SGT-4 data set, with 3492 transcripts at 0.1 significance. Analyzing differential expression for SCB-1-SGB-3 data set at 0.01 significance, 1516 transcripts were found to show significant differential expression. Of these, a total of 814 were down-regulated and 702 were up-regulated. As per the obtained FC values, among the SCB-1–SGB-3 down-regulated transcripts, 135 exhibited a fold change of < − 5 while 679 transcripts showed a fold change of > − 5. A total of 92 transcripts were up-regulated by < 5 fold while 610 were up-regulated by > 5 fold (Fig. 2).

Fig. 2.

Fig. 2

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Pi chart representing number of up-regulated and down-regulated transcripts in bottom region of control (SCB-1) and GA3-treated (SGB-3) canes (a), and in top region of control (SCT-2) and GA3-treated (SGT-4) canes (b) depending on the fold change

On the other hand, 3492 differentially expressed transcripts were recorded for the SCT-2–SGT-4 data set at 0.1 significance. Further at this significance level, a total of 1589 transcripts were found for the SCT-2–SGT-4 data set. From among these, 324 were down-regulated and 1265 were up-regulated. From among the down-regulated SCT-2–SGT-4 transcripts, 44 showed a fold change of − 5 and less while 280 showed a fold change of > − 5. Also, 684 transcripts were up-regulated by more than fivefold and 581 transcripts showed an up-regulation of less than fivefold (Fig. 2). The expression profiles of overlapping transcripts were further explored by generating heat maps (Fig. 3). As represented in heat maps, significant difference was found, in expression levels of various transcripts of control bottom and top internodes, with respect to the GA3-treated samples. Possibly, these differentially expressing transcripts could have some association in regulation of photosynthesis as genetics and transcriptome analysis have revealed a gibberellin-responsive pathway involved in regulating the photosynthesis (Xie et al. 2016).

Fig. 3.

Fig. 3

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Heat map showing expression level of overlapping transcripts in bottom region of control (SCB-1) and GA3-treated (SGB-3) canes (a), top region of control (SCT-2) and GA3-treated (SGT-4) canes (b)

To get a detailed insight into the functional significance of the identified differentially expressed transcripts, functional analysis was carried out by employing various databases/tools like GO, KEGG (Kanehisa et al. 2012). A KEGG (enrichment) analysis grouped the transcripts into 236 functional categories. From among these, 153 plant-related categories viz. sugar metabolism, carbon fixation, signaling pathways, were sieved out for further analysis. Based on cDNA-SRAP, a total of 24 positive transcripts derived fragments were reported when gibberellins-induced stem tissues were analyzed (Wu et al. 2010a, b). Cheng et al. (2015) also reported gibberellins-induced changes in transcriptome of grapevine.

Based on GO annotation, the 7330 genes were categorized into different ontologies: biological process (BP), cellular component (CC) and molecular function (MF). WEGO plots depict the number/percentage of genes belonging to each ontology and the proportion of genes belonging to different functional groups. For the BP category, maximum representation of genes was obtained under the heads of cellular processes, metabolic process, biological regulation, pigmentation and response to stimulus (Fig. 4). Cell, cell part, organelle and macromolecular complexity exhibited maximum representation under the CC category (Fig. 4). Under the MF category, maximum genes were found to be involved in functions like catalytic activity, binding, antioxidants, structural molecule (Fig. 4). As in the present study too (data not shown), a large number of transcripts (67%) have been either defined as genes of unknown function proteins or unknown genes in a study by Wu et al. (2010a, b).

Fig. 4.

Fig. 4

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Gene ontology categorization of the unigenes based on the ontologies of cellular, molecular and biological components

Function-specific screening and identification of transcripts could be an important resource to detect genetic differences among control and GA3-treated plants and to affect improved sucrose accumulation. Hence, sugar metabolism and/or photosynthesis-related transcripts were picked to obtained transcript subsets of 257 for SCB-1-SGB-3 data set and 256 for SCT-2–SGT-4 data set (data not shown). The number of transcripts in each subset was further brought down by screening transcripts on the basis of difference in read count, selecting transcripts showing maximum difference in number of read counts. A homology search was then carried out using BLASTx analysis tool, to identify transcripts sharing close ancestry with genes of sugarcane and of plants homologous to sugarcane (Tables 2, 3). A total of 26 transcripts from SCB-1–SGB-3 data set (Table 2) and 39 from SCT-1–SGT-4 (Table 3) data set were found to exhibit homology to various C4 plants. Of these, 15 shared high identity with Sorghum, 21 with Zea mays and 12 with Saccharum itself. As reported earlier that stem elongation is normally associated with the level of hormones and interaction among them (Wu et al. 2009, 2010a, b; Liang et al. 2015; Iqbal et al. 2017), it will be interesting to visualize the expression level of such genes. However, the present results so far did not reveal any genes directly linked with synthesis/transport of hormones associated with GA3 (Tables 2, 3). Nevertheless, genes identified in the category of cellular functions might shed some light on this direction (Fig. 4).

Table 2.

BLASTx analysis of screened transcripts (SCB-1 and SGB-3) in homologous C4 and C3 plants

S. no Query SCB_read_counts SGB_read_counts C4 plants with maximum homology Read length Accession no
1 TR61807|c2_g2_i2 42,856.19 25,525.43 Oryza sativa Japonica Group genomic DNA, chromosome 8, BAC clone:OSJNBa0073I05 287 AP005442
2 TR33297|c0_g1_i1 29,077 20,388 Zea mays starch synthase homolog 1 (gss1), mRNA 1124 NM_001111410
3 TR5230|c0_g1_i1 29,077 20,388 Sorghum bicolor hypothetical protein, mRNA 294 XM_002468389
4 TR44491|c0_g3_i1 30,797.7 18,245.69 Saccharum hybrid cultivar R570 clone BAC 170C07 complete sequence 771 KF184732
5 TR80564|c1_g1_i8 30,797.7 18,245.69 Saccharum hybrid cultivar BAC clone Sh15N23, cultivar R570 249 FN431663
6 TR32132|c0_g4_i1 21,877 15,342 Sorghum bicolor hypothetical protein, mRNA 351 XM_002445172
7 TR44084|c0_g1_i1 21,877 15,342 Sorghum bicolor hypothetical protein, mRNA 294 XM_002459944
8 TR82125|c0_g1_i4 6253.76 4953.07 Aegilops tauschii subsp. tauschii uncharacterized LOC109769515 (LOC109769515), ncRNA 1530 XR_002234342
9 TR53825|c0_g1_i5 5607.79 4080.32 PREDICTED: Setaria italica cytochrome P450 88A1-like (LOC101783481), mRNA 1760 XM_004980606
10 TR39853|c0_g2_i1 1293.65 822.84 PREDICTED: Zea mays uncharacterized LOC100280325 (gpm480), transcript variant X1, mRNA 1653 XM_020551520
11 TR16835|c0_g1_i1 2299.63 1191.55 Sorghum bicolor hypothetical protein, mRNA 234 XM_002458711
12 TR44150|c0_g1_i1 1396.61 1109.73 Sorghum bicolor hypothetical protein, mRNA 280 XM_002453824
13 TR74097|c0_g1_i1 1216.97 1067.25 PREDICTED: Zea mays Mannose-1-phosphate guanylyltransferase 1 (LOC100191571), transcript variant X1, mRNA 307 XM_008659792
14 TR17534|c0_g2_i1 1216.97 1067.25 Saccharum hybrid cultivar R570 clone BAC 078K12 complete sequence 270 KF184921
15 TR46532|c0_g2_i1 2299.4 1001.12 Saccharum hybrid cultivar R570 clone BAC 033L20 complete sequence 236 KF184836
16 TR74042|c0_g1_i1 20,711.69 26,377.83 Saccharum hybrid cultivar GT28 cell wall invertase (CWI) gene, complete cds 255 JQ312299
17 TR30540|c0_g2_i2 7646.18 9547.6 Zea mays Photosystem I reaction center subunit XI chloroplastic (umc1974), mRNA 238 NM_001175751
18 TR24557|c0_g2_i1 5891.77 8226.51 Sorghum bicolor hypothetical protein, mRNA 234 XM_002461407
19 TR52722|c0_g1_i1 3143 4906.3 PREDICTED: Zea mays uncharacterized LOC103643675 (LOC103643675), transcript variant X2, ncRNA 278 XR_002266459
20 TR58301|c0_g1_i1 3368.84 4489.01 Saccharum hybrid cultivar R570 clone BAC 028K15 complete sequence 249 KF184834
21 TR78338|c0_g1_i1 1474.97 2122.21 Zea mays phosphoglycerate kinase (LOC100381624), mRNA 267 NM_001174441
22 TR31575|c0_g3_i1 503.69 1071.63 Saccharum hybrid cultivar R570 clone BAC 265I24 complete sequence 352 KF184719
23 TR74776|c0_g1_i1 393.55 606.85 Oryza sativa Japonica Group DNA, chromosome 7, cultivar: Nipponbare, complete sequence 268 AP014963
24 TR79462|c0_g1_i1 219 370 Oryza sativa Japonica Group DNA, chromosome 7, cultivar: Nipponbare, complete sequence 231 AP014963
25 TR44409|c3_g3_i1 183.58 330.88 Saccharum hybrid cultivar BAC clone Sh15N23, cultivar R570 552 FN431663
26 TR17534|c0_g1_i1 69.98 211.81 Saccharum hybrid cultivar R570 clone BAC 026K06 complete sequence 270 KF184774
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Table 3.

BLASTx analysis of screened transcripts (SCT-2 and SGT-4) in homologous C4 and C3 plants

S. no Query SCT_read_counts SGT_read_counts C4 plants with maximum homology Read length Accession no
1 TR27182|c2_g2_i2 15,163.7 25,042.91 Sorghum bicolor voucher BTx623 locus HHU62 genomic sequence 1968 DQ427847
2 TR42347|c3_g4_i1 18,925.16 21,240.99 Zea mays chlorophyll a–b binding protein 4 (LOC100280410), mRNA 774 NM_001317925
3 TR54903|c0_g2_i1 14,025 16,163 Oryza sativa Japonica Group genomic DNA, chromosome 6, PAC clone:P0578B12 656 AP003511
4 TR22066|c0_g1_i2 3925.57 8008.62 Zea mays full-length cDNA clone ZM_BFb0213A19 mRNA, complete cds 1532 BT062077
5 TR49686|c0_g1_i3 3654 7157.45 PREDICTED: Oryza brachyantha zeaxanthin epoxidase, chloroplastic (LOC102712906), mRNA 2653 XM_015835992
6 TR22066|c0_g1_i3 2333.43 5791.7 Oryza sativa Indica Group cultivar RP Bio-226 chromosome 3 sequence 1496 CP012611
7 TR53845|c3_g2_i8 3006.03 4937.96 Miscanthus sinensis subsp. condensatus isolate MscT-549_2-13b_1 MSG549_2 marker genomic sequence 263 KM237097
8 TR81388|c0_g1_i2 2789.71 4347.64 Phyllostachys edulis chloroplast chlorophyll a/b binding protein (cab-PhE1) mRNA, complete cds; nuclear gene for chloroplast product 371 EF207229
9 TR65200|c0_g1_i1 1736.71 3370.67 PREDICTED: Zea mays uncharacterized LOC103626396 (LOC103626396), ncRNA 2906 XR_552923
10 TR10420|c0_g1_i1 2425.68 3267.78 Phyllostachys edulis clone cab-PhE3 chloroplast chlorophyll a/b binding protein mRNA, complete cds; nuclear gene for chloroplast product 670 EF405877
11 TR18646|c3_g1_i1 2067 3169 Zea mays clone 280,746 ribose-5-phosphate isomerase mRNA, complete cds 1495 EU964671
12 TR46215|c0_g1_i2 1156.11 2866.01 PREDICTED: Zea mays uncharacterized LOC103647078 (LOC103647078), ncRNA 3522 XR_562638
13 TR53845|c3_g2_i2 1626.86 2248.53 PREDICTED: Oryza brachyantha chlorophyll a–b binding protein of LHCII type 1-like (LOC102716716), transcript variant X2, mRNA 382 XM_015841175
14 TR71094|c2_g1_i1 825 2051 Sorghum bicolor hypothetical protein, mRNA 2667 XM_002468043
15 TR22066|c0_g1_i1 888.83 1993.81 Triticum aestivum clone UCDTA00738 genomic sequence 1517 HQ390287
16 TR46215|c0_g1_i1 818.63 1756.7 Zea mays PCO061836 mRNA sequence 3607 AY108559
17 TR66859|c0_g1_i2 847.8 1281.11 Oryza sativa Japonica Group DNA, chromosome 3, cultivar: Nipponbare, complete sequence 3220 AP014959
18 TR49673|c0_g1_i2 484.29 837.25 Zea mays CL1267_2 mRNA sequence 1456 AY109361
19 TR81388|c0_g1_i5 566.42 803.58 Sorghum bicolor hypothetical protein, mRNA 358 XM_002466818
20 TR65200|c0_g1_i2 490.29 740.33 Miscanthus sinensis glucose-6-phosphate isomerase mRNA, complete cds 4898 HM062765
21 TR81388|c0_g1_i1 474.31 735.75 Zea mays mRNA for type II light-harvesting chlorophyll a/b-binding protein 272 X68682
22 TR4782|c0_g2_i3 410.6 687.26 Sorghum bicolor hypothetical protein, mRNA 4708 XM_002460152
23 TR22066|c0_g1_i4 293.86 616.6 Zea mays subsp. mays Mu transposon insertion Mu1007762 flanking sequence 1238 FJ910033
24 TR25218|c0_g1_i2 320.18 603.28 Sorghum bicolor hypothetical protein (SORBIDRAFT_0013s009080) mRNA, complete cds 2141 XM_002489136
25 TR85381|c0_g1_i1 294 526.29 Sorghum bicolor microsatellite mSbCIR238 sequence 3353 JQ031021
26 TR65845|c0_g3_i3 499.13 522.46 Saccharum spp. complex hybrid trehalose-6-phosphate synthase 1 (TPS1) mRNA, partial cds 3307 EU761244
27 TR36575|c0_g2_i1 395.24 508.78 Zea mays full-length cDNA clone ZM_BFc0052E07 mRNA, complete cds 228 BT039933
28 TR14322|c0_g3_i2 121.96 255.99 Zea mays clone Contig 962.F mRNA sequence 1437 BT019289
29 TR2546|c0_g1_i4 156.04 248.49 Sorghum bicolor hypothetical protein, mRNA 1136 XM_002461019
30 TR75023|c0_g1_i1 119.28 225.36 PREDICTED: Setaria italica phosphoglycerate kinase, cytosolic-like (LOC101775268), mRNA 765 XM_004966259
31 TR2499|c2_g1_i1 56 196 Zea mays Mu transposon insertion mu1017502 flanking sequence 1561 HQ137431
32 TR44379|c0_g2_i1 42.18 190.74 Sorghum bicolor hypothetical protein, mRNA 3165 XM_002455876
33 TR65845|c0_g3_i4 87.15 187.03 Saccharum spp. complex hybrid trehalose-6-phosphate synthase 1 (TPS1) mRNA, partial cds 3382 EU761244
34 TR77567|c0_g1_i2 97.48 184.08 Zea mays clone 1,476,724 mRNA sequence 2217 EU954676
35 TR21085|c0_g1_i1 55.49 159.84 Oryza sativa Japonica Group DNA, chromosome 9, cultivar: Nipponbare, complete sequence 1630 AP014965
36 TR54903|c0_g3_i1 11,014 10,873 Zea mays chlorophyll a–b binding protein 6A (LOC100281791), mRNA 307 NM_001154711
37 TR28583|c2_g1_i1 5825.92 4971.86 Saccharum hybrid cultivar 16-kDa membrane protein (MPS) mRNA, complete cds 967 KF714498
38 TR36575|c0_g3_i1 513.67 499.75 Zea mays glyceraldehyde phosphate dehydrogenase B1 (gpb1), mRNA 228 NM_001305859 XM_008652366
39 TR81648|c0_g1_i1 139 468 Sorghum bicolor hypothetical protein, mRNA 3147 XM_002453240
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Validation of the expression levels of differentially expressed genes

Fold changes as visualized in RNA Seq data were further validated by quantitative real-time PCR (qRT-PCR) with transcripts/DEGs. For this purpose, total RNAs from top and bottom internodes of both GA3-sprayed and control plants at the stage of 210 DAS were utilized. Among the transcripts/differentially expressed genes, those having association with C4 crops like maize and sorghum were selected for the validation. Transcripts from the bottom internode which exhibited association with genes of photosystem I reaction center, phosphogylcerate kinase, Saccharum hybrid cultivar BAC clones and maize transcript variant and top internodal transcripts exhibiting homology with chlorophyll a–b binding protein of maize and rice, hypothetical protein of sorghum and trehalose-6-phosphate synthase of Saccharum spp. hybrid were selected for validation. All nine genes selected showed amplifications (Fig. 5, Table S1). Barring two transcripts (SRBC, MUTV), the fold change in expression was visualized as expected, hence confirming the reliability of transcriptome data. In general, the fold change in expression of genes associated with top region of internodes was much higher than that of bottom internodes, possibly because top portion of canes was in more active stage of growth; whereas bottom internodes were tuned to store the sucrose formed and transported it to sink tissues. From among the transcripts selected from the top internodes, maximum fold change in expression was observed with transcripts annotated as Sorghum bicolor hypothetical protein whereas from among those of bottom intermodal sample, an uncharacterized transcript variant of Zea mays showed highest fold change (Fig. 5). Higher expression of two transcripts associated with chl a–b binding proteins (MCBP, OCBP) and trehalose-6-phosphate synthase (STPS) in GA3-treated top samples perhaps also signified the overexpression of these genes under the influence of GA3. In case of bottom internodes, the influence of GA3 on expression of phosphoglycerate kinase (MPGK) was indicative of sucrose storage in bottom internodes.

Fig. 5.

Fig. 5

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Validation of differential expression of transcripts/genes observed between top and bottom internodes under the influence of GA3 through qRT-PCR analysis. The Y-axis represents relative expression of genes over their corresponding controls. a GA3-treated bottom internodes over control bottom, b GA3-treated top internodes over control top. The X-axis represents the different genes annotated as encoding Saccharum R570 BAC clone (SRBC), maize uncharacterized transcript variant (MUTV), maize photosystem I reaction center (MPRC), maize phosphoglycerate kinase (MPGK), Japonica group DNA (JGD), maize Chl a–b binding protein (MCBP), Oryza Chl a–b binding protein (OCBP), sorghum hypothetical protein (SHP) and Saccharum trehalose-6-phosphate synthase (STPS). The means of relative expression level ± standard error (SE) with three replicates were shown on the column diagram

These results were in agreement with our transcriptome data, suggesting that transcriptome sequencing was correct and genes were authentically involved in the influence of GA3 on photosynthesis and accumulation of sucrose in cane stalk. One can argue whether these GA3-induced transcripts have any functional links between sugar and hormone signaling network as proposed by Ljung et al. (2015), which would require a detailed knock-out study to decipher the role of each and every highly differentially expressing gene. As reported earlier, the most important impact of GA is in causing increase in internodal length (Wu et al. 2010a, b; Verma et al. 2017), and thus, the key regulatory enzyme/gene in GA synthesis pathway needs to be targeted, to balance the amount of GA, which in turn could regulate plant growth and development (Wu et al. 2016; Zhang et al. 2017). Higher fold change in expression of transcripts associated with sucrose synthesis and transport (cell wall invertase and kinase) in bottom internodes and in photosynthesis-related transcripts (Chl a–b binding proteins) of top internodes indicated differential role of the two distinct portions of canes in transport and accumulation of sucrose in sugarcane (Tables 2, 3).

Conclusions

The data generated in this study have enriched the sugarcane transcriptome resource and will be useful for further deliberation and functional genomic studies in genus Saccharum and family Poaceae. The transcripts, hence, identified may be employed to elucidate the factors/genes affecting sucrose accumulation in sugarcane. Owing to complexity and allogenetic background of sugarcane genome, such transcriptome data have provided first hand information regarding transcripts and genes involved in various metabolic pathways. We have validated some differentially expressing transcripts identified from both top and bottom internodes which depicted intended variation in expression of genes having association with photosynthesis and carbohydrate metabolism. More are in pipeline, which need to be validated and further incorporated for better understanding of the GA3-induced perturbation in source–sink dynamics in sugarcane. Nevertheless, our results and the resources generated in this study will facilitate discovery of new genes and thereby accelerate functional genomic research in sugarcane per se.

Nucleotide Accession Number(s)

The data discussed in this publication have been deposited in NCBI’s SRA module and are accessible through Accession Nos. SRR9113179 (top control internodes), SRR9113177 (Top GA3-treated internodes), SRR9113178 (bottom control internodes) and SRR9113176 (bottom GA3-treated internodes).

Electronic supplementary material

Below is the link to the electronic supplementary material.

Table S1 Primers for qRT-PCR validation (13.7KB, docx)
13205_2019_1908_MOESM2_ESM.xls (22.5KB, xls)

Table S2 The minimum information for publication of quantitative real-time PCR experiment (MIQE) sheet

Acknowledgements

Authors are thankful to the Director for providing necessary facilities to carry out the work. KR and IV are grateful to CSIR and DST-SERB, respectively, for financial support in form of fellowships. Facilities utilized developed under DST-SERB project (SERB/SR/SO/PS/36/2012) are duly acknowledged.

Compliance with ethical standards

Conflict of interest

Authors declare that they have no conflict of interest.

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Associated Data

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

Supplementary Materials

Table S1 Primers for qRT-PCR validation (13.7KB, docx)
13205_2019_1908_MOESM2_ESM.xls (22.5KB, xls)

Table S2 The minimum information for publication of quantitative real-time PCR experiment (MIQE) sheet

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