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. 2020 May 7;10(6):236. doi: 10.1007/s13205-020-02226-0

Comparative analysis of drought-responsive transcriptomes of sugarcane genotypes with differential tolerance to drought

A Selvi 1,✉, K Devi 1, R Manimekalai 1, P T Prathima 1
PMCID: PMC7203378  PMID: 32399386

Abstract

Water stress causes considerable yield losses in sugarcane. To investigate differentially expressed genes under water stress, two sugarcane genotypes were subjected to three water-deficit levels (mild, moderate, and severe) and subsequent recovery and leaf transcriptome was generated using Illumina NextSeq sequencing. Among the differentially expressed genes, the tolerant genotype Co 06022 generated 2970 unigenes (p ≤ 0.05, functionally known, non-redundant DEGs) at 2-day stress, and there was a progressive decrease in the expressed genes as the stress period increased with 2109 unigenes at 6-day stress and 2307 unigenes at 10-day stress. There was considerable reduction at recovery with 1334 unigenes expressed at 10 days after recovery. However, in the susceptible genotype Co 8021, the number of unigenes expressed at 2 days was lower (2025) than the tolerant genotype and a further reduction was seen at 6-day stress (1552). During recovery, more differentially expressed genes were observed in the susceptible cultivar indicating that the cultivar has to activate more functions/processes to recover from the damage caused by stress. Comparison of DEGs between all stages of stress and recovery in both genotypes revealed that, the commonly up- and down-regulated genes across different stages were approximately double in the tolerant genotype. The most enriched gene ontology classes were heme binding, peroxidase activity and metal ion binding in the biological process and response to oxidative stress, hydrogen peroxide catabolic process and response to stress in the molecular function category. The cellular component was enriched with DEGs involved in extracellular region followed by integral component of membrane. The KEGG pathway analysis revealed important metabolic activities and functionally important genes involved in mitigating water-deficit stress in both the varieties. In addition, several unannotated genes in important pathways were detected and together may provide novel insights into water-deficit tolerance mechanisms in sugarcane. The reliability of the observed expression patterns was confirmed by qRT-PCR. The results of this study will help to identify useful genes for improving drought tolerance in sugarcane.

Electronic supplementary material

The online version of this article (10.1007/s13205-020-02226-0) contains supplementary material, which is available to authorized users.

Keywords: Drought-responsive genes, RNA Seq, Differential expression, qRT-PCR

Introduction

Water-deficit stress is one of the major environmental constraints limiting agricultural productivity (Ashraf 2010). The period between 60 and 150 days of crop age, known as the formative phase, has been shown to be very sensitive to water deficit stress in sugarcane (Naidu 1976). Water deficit during this phase adversely affects morphological, physiological and biochemical traits, gene and protein expression and subsequently cane and sugar yields (Rocha et al. 2007; Silva et al. 2008; Rodrigues et al. 2009). Drought adaptability is the comprehensive capacity of integrating drought resistance and recovery for adaptation to drought stress and rehydration. While most work focus on drought tolerance by crop plants, not much emphasis has been laid on drought recovery which equally is an important area for coping with stress and revival to complete the growth cycle. The identification and expression profiling of such responsive genes may be helpful to unravel the basic mechanism of stress tolerance (Patade et al. 2010). Recent research has made efficient use of the ‘omic’ approaches to identify transcriptional, proteomic and metabolic networks linked to stress perception and response—not only in the model plant Arabidopsis (Arabidopsis thaliana) but also in crop, garden and woody species (Kumar et al. 2017; Zhang et al. 2017; Bai et al. 2017; Xu et al. 2015a; Paul et al. 2014). To elucidate the mechanism underlying drought tolerance, it is important to determine how plants respond to drought stress and the advent of next generation sequencing platforms has made quantification of such responses easier and precise. With the sugarcane whole genome yet to be deciphered, RNA-Seq studies would be of great aid in elucidating mechanisms involved in stress responses.

In spite of the economic significance of sugarcane, no reference genome of this crop was obtainable until recently (Garsmeur et al. 2018; Zhang et al. 2018; Souza et al. 2019), due to the high complexity of the polyploid genome, that has eluded geneticists and molecular biologists in the past. One of the most common resources used for the study of sugarcane genome and gene expression has been the sequencing of Expressed Sequence Tags (ESTs) (Carson and Botha 2000, 2002). The first large-scale EST collection from a number of libraries that include different plant organs in varied growth phases of sugarcane was generated by the SUCEST project (Vettore et al. 2001, 2003). The database generated by this project identified genes associated with cold stress (Nogueira et al. 2003), oxidative stress (Kurama et al. 2002) and pathogen resistance (Soares-Costa et al. 2002; Mello et al. 2003) and was also used to evaluate the tissue specificity involved in signal transduction (Papini-Terzi et al. 2005) and the tissue specificity of transposons (de Araujo et al. 2005). The SUCEST project provided the first insight into several physiological and biochemical processes that occur in sugarcane. Several gene expression studies have tried to clarify the mechanisms sugarcane adapt to overcome the difficulties encountered in water-deficit regions (Sugiharto et al. 2002; Rocha et al. 2007; Iskandar et al. 2011; Kido et al. 2012; Vantini et al. 2015; Li et al. 2016). These findings highlight the need for new efforts to expand the sugarcane database, as well as to understand and correlate the expression of genes and their products that are involved in the response to water-deficit stress and rehydration. With this concern, gene expression studies were conducted in two sugarcane genotypes, a drought-tolerant (Co 06022) and a drought-sensitive (Co 8021) genotype at formative phase, subjected to various time periods of water stress (2, 6, 10 days) and recovery. RNA-Seq was performed at all stages to understand the response of the sugarcane cultivars to water-deficit stress and recovery. This is the first study that used RNA-Seq technology to verify the response of sugarcane plants during rehydration at the formative phase, which corresponds to the most critical period for water demand. These results should enhance the limited knowledge about the sugarcane genome and its responses to water deficit and recovery.

Materials and methods

Selection of plant materials and RNA extraction

Two sugarcane genotypes (tolerant genotype Co 06022 and susceptible genotype Co 8021) were selected for whole transcriptome sequencing after a comprehensive screening of six genotypes using physiological and biochemical assays for drought stress and recovery in the field and pot studies (Devi et al. 2018, 2019). Genotypes Co 06022 and Co 8021 were planted in pots and at 60 days of planting, gradual drought stress was imposed by withholding water for 2, 6, and 10 days. The plants were then subjected to rehydration for 10 days after severe water stress was imposed for 10 days. The leaf samples were collected from three biological replicates from both control and stressed plants for RNA extraction. RNA was extracted using TRI reagent (Sigma, USA) and the concentration was quantified with NanoDrop ND-1000 spectrophotometer (Thermo Scientific, USA). The quality was checked on a 1.5% agarose gel. RNA quality, integrity and quantity were also determined by spectrophotometer and Agilent RNA Bioanalyser chip.

Construction of cDNA library and sequencing

The cDNA library construction was performed using the NEXTflex Rapid Directional RNA-Seq kit with an average insert size of 200 bp following the manufacturer’s instructions (Bioscientific USA). The purified mRNA was fragmented for 6 min at 95 °C in the presence of divalent cations and reverse transcribed using first strand mix. Second strand cDNA was synthesized and end repaired using second strand synthesis mix. The cDNA was cleaned up using High Prep PCR cleanup beads (solid-phase reversible immobilization). The Illumina adapters were ligated to the cDNA after end repair and addition of A-base. Solid-phase reversible immobilization (SPRI) cleanup was performed post ligation. The library was amplified using 15 cycles of PCR for enrichment of adapter ligated fragments. The prepared libraries were quantified using Qubit Fluorometer (Invitrogen, USA) and validated for quality by running an aliquot on High Sensitivity Tape Station (Agilent Technologies, USA). The transcriptome library was sequenced on Illumina Nextseq500 sequencing system to obtain 150-bp paired-end read data. The sequencing was performed at the Genotypic Technology Pvt. Ltd., Bangalore.

Data filtering and sequence annotation

Raw reads obtained after sequencing were subjected to adapter removal and low-quality base filtering to obtain processed reads. De novo assembly of the processed reads was carried out using Trinity (https://trinityrnaseq.sourceforge.net/) with default K-mer = 25. Transcript clustering was done using CD-HIT-v4.5.4 at 95% identity and 95% query coverage. The assembled transcripts were annotated using NCBI BLAST with the proteins of Viridiplantae from Uniprot database. Each annotated sequence may have more than one GO (Gene Ontology) term assigned for different GO categories (Biological process, Molecular function and Cellular component) or in the same category.

Differential gene expression (DGE) analysis

Transcripts of both control and stressed samples were combined to the size ≥ 300 bp and were clustered together using CD-HIT at 95% identity. Master control transcriptome data (unigenes) were generated. RPKM (Reads per kilo base pair million) measurement is a sensitive approach by which expression level of low-quality transcripts can be detected using read count as the fundamental basis. For RPKM measurement, reads were first aligned using “Bowtie2 tool” to generate the read count profile. The clustered transcripts were used as master reference for carrying out differential gene expression (DGE) analysis by employing a negative binomial distribution model with DeSeq v1.8.1 package (https://www-huber.embl.de/users/anders/DESeq/). The expression profiles for those transcripts expressed only in control, expressed in both control and stress and expressed only in stress were generated. P significant transcripts were obtained for both control and stress samples. Genes with fold change (FC = log fold change to the base 2) ≥ 2 to ≤ 5 and ≤ − 1.5 to ≥ − 5.0 were considered as up- and down-regulated.

Results

Genotype selection for transcriptome profiling and RNA isolation

Two sugarcane genotypes (tolerant genotype Co 06022 and susceptible genotype Co 8021) were selected for whole transcriptome analysis by screening for water-deficit stress in pot culture experiments at different time points viz. 2, 6, 10 days after stress as well as recovery on the basis of physio-biochemical properties and semi-quantitative gene expression analysis (Devi et al. 2018, 2019). The phenotypic expression of Co 8021 (V1) and Co 06022 (V2) at different stress conditions is depicted in Fig. 1. For RNA seq analysis, total RNA was extracted from control, drought-stressed (2, 6 and 10 days) and recovery (10 days) leaf tissues. The quantity of RNA ranged from 1572 to 5856 ng/ul. Samples with spec values of 1.92–2.38 and RIN values ranging from 8.2 to 9.4 indicating good QC integrity were selected for library preparation.

Fig. 1.

Fig. 1

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Phenotypic expression of Co 8021 (V1) and Co 06022 (V2) at different stress conditions

De novo assembly and transcriptome annotation

Using an Illumina paired-end sequencing platform, 175.92 million reads from tolerant genotype Co 06022 and 158.34 million reads from susceptible genotype Co 8021 (both control, stressed and recovery tissues) were generated. The data have been submitted in the National Center for Biotechnology Information (NCBI) with accession number PRJNA590595 (https://www.ncbi.nlm.nih.gov/sra/PRJNA590595). Statistics of de novo transcriptome assembly are given in supplementary file 1—Table S1. Transcripts were annotated using NCBI BLAST with the proteins of Viridiplantae of the Uniprot database. Those transcripts with more than 30% identity as cut-off were taken for further analysis. The transcript annotation summary is given in supplementary file 1—Table S2.

Gene ontology classification of the overall transcripts

GO classification of the transcripts was done based on their biological process (BP), cellular component (CC) and molecular function (MF). Uniprot annotations generated 53,257 (tolerant) and 58,271 (susceptible) unigenes in the BP category and comprised of 2641 and 3433 GO terms, respectively. In both the genotypes (Fig. 2 and 3), the largest biological process was DNA integration and in the tolerant genotype, the number of unigenes involved in DNA integration was less compared to the susceptible genotype. This class was followed by regulation of transcription and transcription in the tolerant genotype and in the susceptible genotype, it was vice versa. The CC category comprised of 1357 (tolerant) and 2987 (susceptible) GO terms, which was assigned to 36,496 and 39,240 unigenes, respectively. In this CC category, the largest subgroups were integral component of membrane followed by nucleus, cytoplasm and plasma membrane, both in tolerant and susceptible genotypes. Similarly, the MF category comprised of 2012 (tolerant) and 4342 (susceptible) GO terms, which were assigned to 33,599 and 36,345 unigenes, respectively. ATP-binding unigenes (11.13%; 10.8%) were predominant followed by nucleic acid binding, zinc ion binding and DNA binding in tolerant and susceptible genotypes.

Fig. 2.

Fig. 2

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Gene ontology classification of the overall transcripts for the variety Co 06022 (V2). The results are summarized in three main categories: biological process, cellular component, and Molecular function

Fig. 3.

Fig. 3

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Gene ontology classification of the overall transcripts for the variety Co 8021 (V1). The results are summarized in three main categories: biological process, cellular component, and Molecular function

Identification of differentially expressed genes in response to drought stress and recovery

To identify the differentially expressed genes, we used the RPKM method to compute the expression levels of the unigenes. In the genotype Co 06022, 2970 unigenes were expressed in 2-day stress; 2109 unigenes were expressed in 6-day stress; 2307 unigenes were expressed in 10-day stress and 1,334 unigenes were expressed in 10 days after recovery (statistically significant (p ≤ 0.05), functionally known, non-redundant DEGs). In the susceptible genotype Co 8021, 2025 unigenes were expressed in 2-day stress; 1552 unigenes were expressed in 6-day stress; 2010 unigenes were expressed in 10-day stress and 1,800 unigenes were expressed at 10 days after recovery (statistically significant (p ≤ 0.05), functionally known, non-redundant DEGs). The p significant transcripts summary of Co 06022 and Co 8021 is shown in Fig. 4a, b. Among the differentially expressed genes, 634 were up-regulated (fold change, FC ≥ 2 to ≤ 5) and 586 were down-regulated (fold change, FC ≥  − 1.5 to ≤ − 5) in the tolerant genotype Co 06022 and 297 were up-regulated and 726 were down-regulated in case of susceptible genotype Co 8021 (Supplementary file 2 and 3). Comparison of the expression profiles of both the genotypes at different stress levels revealed that the DEGs were preferentially induced under 2 days of stress (307 in Co 06022 and 145 in Co 8021) compared to 6 days of stress and 10 days of stress. In contrast, genes were preferentially repressed under severe stress conditions (64 in Co 06022 and 46 in Co 8021). Under recovery conditions, 4 DEGs were up-regulated and 12 were down-regulated in Co 06022 which was far lesser in comparison to Co 8021 which revealed 44 DEGs that were up-regulated and 51 that were down-regulated at recovery conditions.

Fig. 4.

Fig. 4

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Summary of p significant transcripts that are differentially expressed (FC ≥ 2 to ≤ 5 for up-regulated and FC ≥ − 1.5 to ≤ − 5 for down-regulated transcripts, with p ≤ 0.05) in Co 06022 (a) and Co 8021(b) at different stress levels. The x-axis denotes different stress regimes and the d-axis gives the number of DEGs expressed

The number of unique and overlapping genes for the different treatment durations under water-deficit and recovery conditions was summarized. Venn diagram showing the number of up- and down-regulated genes resulting from the four comparisons performed is represented as overlapping circles in Figs. 5 and 6. The number of unique genes that were up-regulated in the tolerant genotype was higher (2394) compared to susceptible genotype (946) at 2 days of stress. At 6- and 10-day stress, the number of uniquely up-regulated genes increased considerably in susceptible genotype when compared to tolerant genotype. Comparison between all stages in both genotypes indicated that the commonly up- and down-regulated genes were almost double (up 643, 341; down 480, 216 in tolerant and susceptible genotypes, respectively) in the tolerant genotype. Interestingly, susceptible genotype had more number of up-regulated genes (3916) compared to tolerant genotype (2163) at recovery stage; and in contrast, the number of down-regulated genes was high in the tolerant genotype. At 6 and 10 days of stress, the number of down-regulated genes was double in the susceptible genotype compared to the tolerant genotype.

Fig. 5.

Fig. 5

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Unique and overlapping transcripts that are up- (a) and down-regulated (b) in the variety Co 06022 (V2) at different time points compared with the control (2-day stress in blue, 6-days stress in red, 10-day stress in green and 10-day recovery in yellow). The numbers within the intersection are the number of DEGs common between the groups compared and the numbers outside the intersection are the DEGs specific to different time points of water stress and recovery

Fig. 6.

Fig. 6

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Unique and overlapping transcripts that are up- (a) and down-regulated (b) in the variety Co 8021 (V1) at different time points compared with the control (2-day stress in blue, 6-day stress in red, 10-day stress in green and 10-day recovery in yellow). The numbers within the intersection are the number of DEGs common between the groups compared and the numbers outside the intersection are the DEGs specific to different time points of water stress and recovery

Transcript levels of differentially expressed genes among all stages

The expression levels of significant DEGs among all stages are tabulated in Supplementary file 1—Table S3. Under 10 days of stress, the expression profile of cytochrome p450 was up-regulated in tolerant (Co 06022) genotype; while it was down-regulated in susceptible (Co 8021) genotype. The expression profile of cytochrome p450 gene was similar at 2, 6 days of stress and rehydration stages of both genotypes. Microtubule associated protein futsch was highly up-regulated at recovery stage of susceptible genotype while it was down-regulated in tolerant genotype. The stress responsible genes like heat stress transcription factor c-1b, late embryogenesis abundant protein and heat shock 70 kDa protein were up-regulated in tolerant genotype at recovery stage; while, these were down-regulated in susceptible genotype upon rehydration. The drought-responsive gene HSP20 family protein was highly up-regulated in Co 06,022 during 2 and 6 days of stress; whereas, the up-regulation was highest in 10-day stress in Co 8021 and it was highly down-regulated in Co 8021 after rehydration. The expression of peroxidase was down-regulated in susceptible genotype at two-day stress; however, it was up-regulated in tolerant genotype. Some of the genes that showed an immediate up-regulation at 2-day stress in tolerant cultivar as compared to the susceptible cultivar were HSP 20 family protein, heat stress transcription factor c-1b, heat shock 70-kDa protein, disease resistance protein RPM1, soluble inorganic pyrophosphatase-like isoform X2, peroxidase and glutathione S-transferase. At rehydration, some genes such as jasmonate-induced protein homolog, probable protein phosphatase 2c-32 and katE: catalase were highly up-regulated in susceptible genotype; however, ZIM motif family protein, WRKY transcription factor 40 and protein app 1 precursor were highly up-regulated in tolerant genotype. Both peroxidase and carboxypeptidase were up-regulated at recovery stages in Co 06022; whereas in Co 8021, they remained down-regulated.

Gene ontology classification of differentially expressed genes under stress

Under stress, biological process and cellular component categories showed similar pattern of gene expression in both genotypes (Fig. 7a, b); whereas in the molecular function category, susceptible genotype had the highest number of DEGs when compared to tolerant genotype. In the biological process category, heme binding was the largest group followed by peroxidase activity, metal ion binding and ATP binding; while in cellular component category, extracellular region was the largest group followed by integral component of membrane, cytoplasm and plant-type cell wall. In the molecular function category, the susceptible genotype was enriched with 493 unigenes that were involved in response to oxidative stress (GO:0006979), 310 unigenes that were involved in hydrogen peroxide catabolic process (GO:0042744), 272 unigenes that were involved in response to stress (GO:0006950), 151 unigenes that were involved in protein folding (GO:0006457) and 144 unigenes that were involved in response to salt stress (GO:0009651), which was considerably higher when compared to tolerant genotype that had 432, 260, 221, 138 and 115 unigenes, respectively. Among the 3 categories, molecular function category contained most drought-responsive genes.

Fig. 7.

Fig. 7

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a Gene ontology classification of DEGs under stress in the variety Co 06022 (V2). The enriched functional groups of DEGs in the three main categories viz., Biological process, Cellular component and Molecular function are shown in the Figure. b Gene ontology classification of DEGs under stress in the variety Co 8021 (V1). The enriched functional groups of DEGs in the three main categories viz., biological process, cellular component and Molecular function are shown in the figure

Metabolic pathway analysis of differentially expressed genes under water stress

To identify the pathways in which the DEGs were involved, the DEGs were mapped on KEGG pathway database. 11,105 unigenes of Co 06022 and 11,965 of Co 8021 were mapped onto several metabolic pathways. In both tolerant and susceptible genotypes plant–pathogen interaction metabolism involved in “environmental adaptation” was the most significant pathway observed. The top enriched pathways of both genotypes were studied to find genes involved in major metabolic activities. In Co 06022, plant–pathogen interaction 13.17% (1462) represented the largest functional pathway followed by plant hormone signal transduction 7.58% (842), spliceosome 5.66% (629), MAPK signaling pathway 5.39% (599) and ribosome 5.33% (592). In case of Co 8021, the same functional pathways were up-regulated except that the number of transcripts in each category varied. Plant–pathogen interaction metabolism represented the largest functional pathway 14.09% (1686) followed by plant hormone signal transduction 7.35% (880), spliceosome 5.62% (672), ribosome 5.56% (665) and MAPK signaling pathway 5.33% (638) (Fig. 8).

Fig. 8.

Fig. 8

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KEGG enrichment analysis of DEGs during stress in Co 06022 (V2) and Co 8021 (V1). The x-axis denotes the different metabolic pathways and the y-axis shows the number of unigenes involved in each pathway

Unigene validation and expression analysis in response to drought stress

To verify the expression kinetics of the DEGs obtained from the RNA-Seq analysis, a qRT-PCR for selected 20 DEGs (Supplementary file 1—Table S4) was performed. This qRT-PCR was carried out with cDNA synthesized from leaf samples of control and 10 days of stress, the results of which are shown in Table 1. A similar regulation pattern was found in the qRT-PCR analysis except for few genes (Fig. 9). Up-regulation of defensin, WRKY, calmodulin binding protein, dirigent, MYB, wall-associated kinase, heat shock 70 kDa protein and 60 kDa jasmonate-induced protein was observed in both studies with slight changes in fold expression levels. Similarly, down-regulation of peroxidase, xyloglucan endotransglucosylase/hydrolase, meiosis 5 and cytochrome b559 subunit alpha (PSII reaction center subunit V) was found in both studies.

Table 1.

Comparison between qRT-PCR and RNA Seq data of 10-day stress

S.No Genes Co 8021 (V1) Co 06022 (V2)
RNA Seq qRT RNA Seq qRT
1 Defensin 4.7 1 4.5 1.4
2 ACC Synthase − 2.7 0.6 3.3 0.3
3 Lipoxygenase − 4.3 4 − 3.5 1.4
4 WRKY 3.9 4.7 3.2 2.7
5 Calmodulin-binding protein 2.8 3.4 3.2 2.1
6 Dirigent 2.2 8.1 5.4 17.5
7 MYB 3.4 4.2 5 2.9
8 ERD4 − 3.9 3.4 2.4 4.2
9 Invertase − 4.4 − 1.8 5.1 − 5.4
10 Peroxidase − 1.7 − 3.1 − 5.4 − 3.2
11 Zinc finger protein 2.4 − 3.3 − 5.5 − 1.5
12 peptide/nitrate transporter − 3.6 1.7 − 3.9 0.2
13 wall-associated kinase 5.2 0.7 6.7 0.3
14 Glutathione S-transferase − 1.5 0.3 − 1.2 0.1
15 Disease resistance protein RGA2 − 1.2 1.3 − 1.4 4.1
16 Heat shock 70 kDa protein 3 2.4 4.1 2
17 60 kDa jasmonate induced protein 5.7 2.8 5.5 1.6
18 Xyloglucan endotransglucosylase/hydrolase − 3.5 − 3.9 − 4.1 − 5.1
19 meiosis 5 − 4.4 − 5.3 − 3.9 − 4.9
20 cytochrome b559 subunit alph (PSII reaction center subunit V) − 4 − 2.2 − 4.7 − 3.8
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(−) symbol indicates down-regulation

Fig. 9.

Fig. 9

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Relative expression of qRT-PCR and RNA Seq data of 10-day stress samples. In qRT-PCR each bar represents the average of three replicates and error bars indicate SE. The x-axis denotes the genes used for expression analysis by qRT-PCR and RNA Seq, y-axis gives the relative expression of the genes

Discussion

In plants, exposure to drought and re-watering triggers a wide range of responses at molecular, physiological and biochemical levels, to cope up with drought stress and expedite recovery mechanisms upon rehydration, thereby leading to normal growth (Chaves et al. 2003; Umezawa et al. 2010). Drought stress has become a common phenomenon in sugarcane agriculture owing to unpredictable monsoons and climate change. Water deficit at early stages of sugarcane, i.e., the formative phase (60–150 days) can cause inhibition of stalk growth, leaf growth, leaf drying and senescence and impair the overall growth of sugarcane plants. Prolonged water-deficit stress leads to inhibition of photosynthesis with an associated lowering of cane and sucrose yields (Robertson et al. 1999). Under drought stress, gene expression alterations induce cellular adaptation resulting in complex defence mechanisms that play crucial role in mitigating drought stress (Zheng et al. 2004). To comprehend the mechanisms by which plants recognize environmental signals and transfer them to cells that are active in adaptive responses is very important for the improvement of tolerant cultivars. Our previous study attempted to understand physio-biochemical responses under drought stress as well as rehydration in sugarcane genotypes could more accurately indicate the growth stage that is more susceptible to stress, degree of stress and the reliability of the sugarcane cultivars for their tolerance capacity (Devi et al. 2018, 2019). Based on the study, two genotypes that were tolerant (Co 06022) and susceptible (Co 8021) to water-deficit stress were selected. The genotypes were subjected to the same extent of drought stress and recovery and evaluated for the overall molecular mechanisms through RNA-Seq analysis. The responses differed greatly between the susceptible Co 8021 and the tolerant Co 06022 genotypes in terms of the number of genes expressed in each stage of stress, their regulation pattern and number of genes in the pathways involved in drought stress responses. Additionally, the qRT-PCR analysis confirmed differential expression of functional and regulatory genes among the susceptible and tolerant genotypes.

In the present study, DEGs were identified and annotated (Supplementary file 1—Table S1), which allowed us to further elucidate drought resistance mechanisms of sugarcane. Furthermore, no previous studies on the transcriptome analysis of sugarcane under recovery have been performed which makes this study unique for candidate gene identification for drought tolerance at the transcriptome level. RNA seq analysis resulted in 1,22,952 transcripts in the tolerant genotype (Co 06022) and 1,11,178 transcripts in the susceptible genotype with an average size of 500 bp. Similarly, Belesini et al. (2017) reported 177,509 transcripts for the tolerant cultivar SP81-3250, with an average size of 833 bp and 1,85,153 transcripts with an average size of 816 bp for the sensitive cultivar RB855453. Gene ontology is an international standardized gene functional classification system with three ontologies viz., biological process (BP), molecular function (MF), and cellular component (CC) reported by Wang et al. (2010). Each ontology offers a broad description of the functional properties of the genes. In tolerant genotype Co 06022 (control, 2, 6, 10 days of stress and 10 days of recovery), the biological processes (BP) category consisted of 2641 GO terms which were assigned to 53,257 unigenes. The cellular component (CC) category comprised of 1357 GO terms, which were assigned to 36,496 unigenes. The molecular function (MF) category had 2012 GO terms, which were assigned to 33,599 unigenes. In the susceptible genotype Co 8021 (control, 2, 6, 10 days of stress and 10 days of recovery), the biological processes (BP) category consisted of 3433 GO terms which were assigned to 58,271 unigenes. The cellular component (CC) category comprised 2987 GO terms, which were assigned to 39,240 unigenes. The molecular function (MF) category consisted of 4342 GO terms, which were assigned to 36,345 unigenes. Similar findings were obtained by Santana et al. (2017) in sugarcane leaves and roots exposed to progressive osmotic stress using transcriptional profiling and by Belesini et al. (2017) in two cultivars, tolerant and sensitive to prolonged water deficit (30, 60, and 90 days) using de novo transcriptome assembly. These observations suggest that the common genes belonging to these categories are involved in basic events that are crucial for the maintenance of sugarcane, irrespective of its tolerant or sensitive nature and the variation of expression of these genes in one or the other cultivar is responsible for the diverse features when exposed to water deficit and recovery.

Belesini et al. (2017) reported that at 2 days, there were a larger number of induced genes in both tolerant and sensitive cultivars including the categories growth, antioxidant, and virion part. It was observed that the genes responsible for repair of the damages caused by reactive oxygen species (ROS) as a result of stress (antioxidant mechanisms) were expressed in more numbers. This study also reports more number of induced genes in both tolerant and susceptible genotypes that are involved in response to oxidative stress, hydrogen peroxide catabolic processes, response to salt stress and peroxidase activity. The production of ROS in the cell in response to different types of stress represents a threat to the cell and the ROS acts as a signal for triggering stress response and various defense mechanisms and pathways to mitigate stress (Knight and Knight 2001). ROS can, thus, be seen as cellular indicators of stress and as second messengers participating in the signal transduction pathways involved in the stress responses (Mittler 2002).

Among the DEGs (Fig. 4a, b), the up-regulation of genes in response to water deficit stress was much higher at initial stage compared to 6 and 10 days of stress in both genotypes and during recovery, the susceptible genotype Co 8021 had more expressed genes compared to tolerant genotype Co 06022. The up-regulation of genes such as malate synthase, lipoxygenase, 3-ketoacyl-CoA synthase, trehalose 6-phosphate phosphatase, ATP-dependent 6-phosphofructokinase (ATP-PFK), hexosyltransferase and arogenate dehydratase involved in metabolic pathways related to carbohydrate metabolism, lipid metabolism, fatty acid biosynthesis, trehalose biosynthesis, carbohydrate degradation, pectin biosynthesis and amino acid biosynthesis, respectively, was observed in both the genotypes during stress. Carbohydrate metabolism is associated with sucrose accumulation in sugarcane and many studies suggest a possible overlap of genes with drought stress signaling pathway (Papini-Terzi et al. 2009). Genes involved in lipid metabolism helps to protect cells against drought stress by changing the lipid composition of plasma membrane and stress-stimulated fatty acid unsaturation (Allakhverdiev et al. 1999; Wallis and Browse 2002). Protein stabilization and degradation pathways stimulate the management of misfolded, unfolded and accumulated proteins in stress conditions (Kumar et al. 2007). Trehalose is a non-reducing sugar that acts as an osmolyte in most dessication tolerance plants and helps for osmotic adjustment of species, genotypes or cultivars in grasses (El- Bashiti et al. 2005; Hongbo et al. 2006). Additionally, phenylalanine ammonia-lyase (EC 4.3.1.24), an enzyme in the phenylpropanoid metabolism, was up-regulated during stress conditions (Supplementary file 3). The phenylpropanoid pathway is essential for the synthesis of secondary metabolites that helps plant development and environmental interactions for defense mechanisms (Hamberger and Hahlbrock 2004). Furthermore, several down-regulated genes such as cellulose synthase, cytochrome c oxidase subunit 1, starch synthase, chloroplastic/amyloplastic (EC 2.4.1.-), zeaxanthin epoxidase, chloroplastic (EC 1.14.13.90), ferredoxin-dependent glutamate synthase, chloroplastic (EC 1.4.7.1) (Fd-GOGAT), E3 ubiquitin-protein ligase and pyruvate kinase (EC 2.7.1.40) were involved in plant cellulose biosynthesis, oxidative phosphorylation, starch biosynthesis, plant hormone biosynthesis, amino acid biosynthesis, protein modification and carbohydrate degradation pathways, respectively, suggesting their involvement in the plant protection under water-deficit conditions. These pathways are important for biomass production and accumulation of sucrose in sugarcane (Wang et al. 2016).

Some stress-related genes detected in this study such as cytochrome P450 participated in homeostasis of phytohormones (Mizutani 2012) and are involved in signal transduction that helps to withstand abiotic stresses (Yan et al. 2016). Also genes like HSP 20 family protein (Zhao et al. 2018), late embryogenesis abundant protein (Hanin et al. 2011), heat stress transcription factor c-1b (Mishra et al. 2002), heat shock 70-kDa protein (Borges et al. 2001), disease resistance protein RPM1 (Grant et al. 2000), antioxidant enzymes such as peroxidase and catalase (Miller 2012) and glutathione S-transferase (Xu et al. 2015b) reported to be involved in the abiotic stresses and defense processes were identified in this study. Furthermore, some important genes like jasmonate-induced protein homolog and ethylene-responsive element-binding protein were identified which are known to play a key role in plant abiotic stress tolerance such as salt and drought (Kazan 2015). At recovery, many of these genes were up-regulated in susceptible genotype when compared to tolerant genotype (Figs. 5 and 6) and this observation suggests that more number of genes are required to stimulate recovery from stress especially in the susceptible sugarcane genotype.

Pathway analysis of the DEGs indicated that more number of genes was involved in plant–pathogen interaction metabolism, plant hormone signal transduction, spliceosome, ribosome and MAPK signaling pathways in both genotypes (Fig. 8). Major genes that were involved in the plant–pathogen interaction, included calcium-binding protein CML, calcium-dependent protein kinase, cyclic nucleotide gated channel and disease resistance protein RPM1. The calcium sensor proteins and kinases have been identified to play diverse roles acting as cellular signaling cascades by regulating a number of target proteins and also as regulators for downstream gene expression in both biotic and abiotic stress responses. The receptor kinases are, therefore, more important in perceiving a wide range of extracellular signals and activate signal transduction pathways. More recently, Liu et al. (2018) identified DEGs involved in plant hormone signal transduction as the most important pathway under drought stress in related genera of sugarcane “Saccharum narenga”. In our study, the major genes involved in plant hormone signal transduction were auxin-responsive protein, IAA, jasmonate ZIM domain-containing protein and saur family protein playing important role in defense mechanisms. However, under long-term drought stress, apart from the plant hormone signal transduction pathway being more significant, other pathways like starch and sucrose metabolism (Zhu et al. 2017) were also reported to play major role in drought response. Interestingly, “ribosome” pathway was enriched with 118 genes in susceptible genotype and 129 genes in tolerant genotype and “spliceosome” pathway with 78 genes in susceptible and 84 genes in tolerant genotypes in the present study. The results emphasise the importance of translational machinery under the control of ribosomes and the role of spliceosomal complex in alternate splicing of mRNAs, thereby resulting in proteins with altered structures, functions and localizations for effective stress tolerance (Wang et al. 2020; Liu et al. 2018; Xu et al. 2018).

Conclusions

Based on our previous morphological, physiological and biochemical analyses (Devi et al. 2018; 2019), two sugarcane genotypes (Co 8021 and Co 06022) were selected and used for RNA-Seq to reveal responses to drought stress and recovery. Comparative transcriptomic analysis of the two sugarcane genotypes revealed that genes involved in antioxidative pathway, carbohydrate biosynthesis and degradation pathway, phenylpropanoid pathway, lipid metabolism, plant hormone and amino acid biosynthesis pathway could play important role in the drought defense mechanisms. Furthermore, recovery studies revealed that more number of genes is required to induce recovery from stress specifically in the susceptible genotype Co 8021. Our results provide more information about differentially expressed genes that are expressed in different stages of stress and recovery and genes that are expressed in all stages of stress. Taken together, candidate genes involved in the drought responses and recovery of two sugarcane genotypes were identified, and this will be more useful in sugarcane improvement programs to develop drought-resistant genotypes.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (XLSX 269 kb) (269.9KB, xlsx)
Supplementary file2 (XLSX 230 kb) (230.1KB, xlsx)
Supplementary file3 (DOCX 28 kb) (22.8KB, docx)
Supplementary file4 (DOCX 14 kb) (12.8KB, docx)

Acknowledgements

The authors gratefully acknowledge for financial support and facilities provided by the Director, Sugarcane Breeding Institute, Coimbatore and also thank Genotypic Technology Pvt. Ltd., Bangalore for the transcriptome sequencing analysis.

Author contributions

AS: conceptualization, project administration, data analysis and interpretation of the data, supervision, writing, review and editing. KD: laboratory experiments, methodology, data analysis, validation, writing, review and editing. RM: methodology for RNA Seq, analysis and interpretation of the data. PTP: data analysis, interpretation of the data, methodology for validation, review and editing.

Data availability

All the clean reads have been submitted as sequence read archive (SRA) in NCBI with the BioProject ID- PRJNA590595.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest in this publication.

Contributor Information

A. Selvi, Email: selviathiappan@yahoo.co.in

K. Devi, Email: biotechdevi@gmail.com

R. Manimekalai, Email: rmanimekalaiicar@gmail.com

P. T. Prathima, Email: prathimasambandam@gmail.com

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Associated Data

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

Supplementary Materials

Supplementary file1 (XLSX 269 kb) (269.9KB, xlsx)
Supplementary file2 (XLSX 230 kb) (230.1KB, xlsx)
Supplementary file3 (DOCX 28 kb) (22.8KB, docx)
Supplementary file4 (DOCX 14 kb) (12.8KB, docx)

Data Availability Statement

All the clean reads have been submitted as sequence read archive (SRA) in NCBI with the BioProject ID- PRJNA590595.

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