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. 2023 Dec 29;14(1):24. doi: 10.1007/s13205-023-03867-7

Comparative genome-wide characterization of salt responsive micro RNA and their targets through integrated small RNA and de novo transcriptome profiling in sugarcane and its wild relative Erianthus arundinaceus

Palanisamy Vignesh 1,#, Channappa Mahadevaiah 1,2,✉,#, Kannan Selvamuthu 1, Huskur Kumaraswamy Mahadeva Swamy 1, Venkatarayappa Sreenivasa 1, Chinnaswamy Appunu 1,✉
PMCID: PMC10756875  PMID: 38162015

Abstract

Soil salinity and saline irrigation water are major constraints in sugarcane affecting the production of cane and sugar yield. To understand the salinity induced responses and to identify novel genomic resources, integrated de novo transcriptome and small RNA sequencing in sugarcane wild relative, Erianthus arundinaceus salt tolerant accession IND 99-907 and salt-sensitive sugarcane genotype Co 97010 were performed. A total of 362 known miRNAs belonging to 62 families and 353 miRNAs belonging to 63 families were abundant in IND 99-907 and Co 97010 respectively. The miRNA families such as miR156, miR160, miR166, miR167, miR169, miR171, miR395, miR399, miR437 and miR5568 were the most abundant with more than ten members in both genotypes. The differential expression analysis of miRNA reveals that 221 known miRNAs belonging to 48 families and 130 known miRNAs belonging to 42 families were differentially expressed in IND 99-907 and Co 97010 respectively. A total of 12,693 and 7982 miRNA targets against the monoploid mosaic genome and a total of 15,031 and 12,152 miRNA targets against the de novo transcriptome were identified for differentially expressed known miRNAs of IND 99-907 and Co 97010 respectively. The gene ontology (GO) enrichment analysis of the miRNA targets revealed that 24, 12 and 14 enriched GO terms (FDR < 0.05) for biological process, molecular function and cellular component respectively. These miRNAs have many targets that associated in regulation of biotic and abiotic stresses. Thus, the genomic resources generated through this study are useful for sugarcane crop improvement through biotechnological and advanced breeding approaches.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03867-7.

Keywords: Sugarcane, miRNA, Transcriptome, Small RNA, Salinity, miRNA targets

Introduction

MicroRNAs (miRNAs) are endogenous, single-stranded non-coding regulatory RNA molecules with a length of 18–24 bp involved in post-transcriptional regulation of gene expression in eukaryotes (Zhang et al. 2006; Yang et al. 2021a). The plant miRNA-guided post-transcriptional regulation involves the recognition of targets based on their complementarity or near complementarity between miRNA and their target mRNA (Brodersen et al. 2008). Plant miRNA interacts with 3′ UTR, coding regions and 5′ UTR regions of target genes (Hausser et al. 2013) to regulate the gene expression either through mRNA cleavage or translational repression (Gu and Kay 2010; Orang et al. 2014; Li et al. 2018). The biogenesis of miRNA in plants involves precise, complex and highly regulated signalling networks (Wang et al. 2019a; Gao et al. 2021) and, there are several evidences to elucidate the role of miRNA in regulating the biotic and abiotic stresses responses in many plant species (Sun et al. 2015; Aravind et al. 2017; Fu et al. 2017; Xie et al. 2017; Zhang et al. 2017; Parmar et al. 2020; Yang et al. 2020).

Salinity and saline water are major constraints in crop production in arid and semi-arid climatic regions (Manoj et al. 2019; Hassani et al. 2020; Hussain et al. 2020). Each day, around 2000 hectares of saline soil was generated globally in turn responsible for 10–25% yield reduction in many crops (Shahid et al. 2018). Salt stress causes two kinds of stresses to plants, namely the osmatic and ionic stresses (Munns and Tester 2008). The salt-induced osmatic stress causes physiological drought stress to plants by reducing the availability of soil moisture. The ionic stress causes the excessive accumulation of salts in tissues such as sodium and chlorine resulted in plasmolysis and cell death (Katsuhara 1997; Huh et al. 2002). The sessile plants exhibits three types of tolerance mechanisms such as tolerance to osmotic stress, toxic ion exclusion and tissue tolerance (Munns and Tester 2008). These tolerant mechanisms are governed by complex, precise and genotypic-specific networks of genes, moderating the responses to salinity stresses at signalling and metabolic pathways (Gupta and Huang 2014; Parihar et al. 2015; Bandehagh and Taylor 2020; Shahid et al. 2020). Several reports has shown that miRNA regulating the salinity stress responses in plant species (Xie et al. 2017; Parmar et al. 2020; Yang et al. 2020).

The significance of miRNA in temporal and spatial regulation of their target gene expression in many metabolic pathways are well studied in many plant species. The selective biogenesis of miRNA, their transportation, miRNA mediated gene regulation and miRNA degradation are highly specific and governed by network of several genes in responses to environmental stimuli (Jung et al. 2015; Von Born et al. 2018; Tolstyko et al. 2019). The miRNAs are regulating the gene expression by interacting with 5′ UTR and coding regions resulting in down regulation of the target genes (Fang and Rajewsky 2011) and modulation of protein turnover through transcriptional repression by interacting with 5′ UTR regions (Lytle et al. 2007). The interaction of miRNA with 3′ UTR regions resulted in translational upregulation of target genes (Vasudevan and Steitz 2007) and miRNA mediated upregulation of target genes (Stevens 2008; Vasudevan 2012; Orang et al. 2014). The miRNA and their target interactions in NGS based genome-wide miRNA-mRNA expression studies were categorized into four types of interactions viz., both miRNA and their targets were upregulated as Type-1 interaction, down regulation of both miRNA and their target genes as Type-2, neutral expression as Type-3 and opposite expression as Type-4 interactions (Aravind et al. 2017). The miRNA regulating their target genes related to abiotic stresses were reported in crop species such as miR164b and OsmiR535 in rice (Jiang et al. 2019; Yue et al. 2020), miRNVL5 in cotton (Gao et al. 2016), miR172a in soybean (Pan et al. 2016), miR172a and miR172b in rice and wheat (Cheng et al. 2021) and miR528 in Bentgrass (Yuan et al. 2015). With advent of NGS, now it easier to dissect out the genome-wide miRNA and their targets associated with regulation of biotic and abiotic stresses and it has been demonstrated in many crop species (Parmar et al. 2020; Yuan et al. 2020; Zhou et al. 2020; Bohra et al. 2021; Li et al. 2021a; Yang et al. 2021a, b).

Erianthus arundinaceus [Retzius] Jeswiet) is a cane forming wild relative of sugarcane (Welker et al. 2015, 2019) and harbours many genes for agronomic traits and tolerances to biotic and abiotic stresses (Uwatoko et al. 2011; Augustine et al. 2015a,b; Narayan et al. 2019, 2021; Clarancia et al. 2020; Swathik Clarancia et al. 2021; 2023). E. arundinaceus is a cross-compatible with sugarcane and has been utilized in our sugarcane breeding programmes, and resulted in identification many genetic stocks with high biomass production potential and tolerant to biotic and abiotic stresses (Kumar et al. 2015; Premachandran et al. 2017; Wang et al. 2019b; Meena et al. 2020). Sugarcane is a glycophyte and salinity stress directly affect the accumulation of biomass and sucrose at all stages of crop growth (Saxena et al. 2010; Yunita et al. 2020; Zhao et al. 2020). The transcriptome and miRNA genomic resources for distant wild relatives such as salinity tolerant E. arundinaceus and salt-sensitive sugarcane cultivars help to decipher the role of miRNA in regulating gene expression and salt responsive metabolic pathways. Hence, we adopted the integrated comparative small RNA and de novo transcriptome analysis to understand the miRNA mediated post-transcriptional regulation of salt responsive genes and pathways in E. arundinaceus genotype IND 99-907 and salt-sensitive sugarcane genotype Co 97010. The accession IND 99-907 collected from the saline soil of Ernakulum district, Kerala (Nair and Somarajan 2003) and characterized for salt tolerance through morphological, anatomical, physiological parameters. We have identified the differentially expressed salt responsive genes in IND 99-907 and Co 97010 through de novo comparative transcriptome analysis (Vignesh et al. 2021) and the present study aimed at identification of salinity responsive miRNA and their targets through integrated small RNA-transcriptome sequencing approaches. The genomic resources developed through this study is first of kind, between sugarcane and its distant relative E. arundinaceus, and useful for crop improvement through marker-assisted breeding and biotechnological approaches.

Material and methods

Plant material, total RNA isolation, library preparation and small RNA sequencing

The details of the experimental material and methodologies followed for sampling of roots from the stressed and control treatments of the salt tolerant E. arundinaceus accession IND 99-907 and salt-sensitive sugarcane variety Co 97010 were previously described in de novo salt transcriptome studies (Vignesh et al. 2021). The total RNA was isolated by using TRIzol reagent followed by quality check using Agilent 2200 Tape Station System (Liu et al. 2014). The RNA integrity (RIN) values ≥ 7.0 were used for library preparation using NEBNext Multiplex Small RNA Library Preparation Kit (New England Biolabs) as per the manufacturer’s instructions and libraries were sequenced using Illumina HiSeq 2500 platform in 1 × 50 bp single end format.

Pre-processing of raw reads and filtering of raw reads

The quality of raw reads of Small RNA were checked using FastQC (Andrews 2015) and the adapter sequences (5′ RNA Adapter =  > 5′ GTTCAGAGTTCTACAGTCCGACGATC; 3′ RNA Adapter =  > 3′ AGATCGGAAGAGCACACGTCTGAACTC) were removed using the Cutadapt tool (Martin 2011). Reads with 18 to 30 nucleotides with Phred score above 30 (Q30) were filtered using Trimmomatic (Bolger et al. 2014). Non-regulatory RNAs such as rRNA, tRNA, snRNA and snoRNA were filtered out by aligning clean reads with SILVA (https://www.arb-silva.de/), GtRNAdb (http://gtrnadb.ucsc.edu/) and Rfam database (http://rfam.sanger.ac.uk/) with a maximum mismatch of 0 using bowtie2 (Langmead and Salzberg 2013) and resultant reads were used for miRNA predictions.

Prediction of miRNAs and annotation

The clean reads filtered after removal of non-regulatory RNAs were aligned against the monocot miRNA database of the miRBase (http://www.mirbase.org/) using bowtie program (Langmead et al. 2009) with a maximum mismatch (−n 2) for the prediction of the known miRNA. The unaligned reads were used for prediction of novel miRNA using Sugarcane monoploid genome (Garsmeur et al. 2018) as the reference genome. The abundance of miRNAs in each samples were estimated using SAMtool (Li et al. 2009) and novel miRNAs were identified by using miRDeep2 tool (Friedländer et al. 2012).

Differential expression analysis of miRNA and their target predictions

The differential expression analysis of miRNAs were performed using DESeq2 (Love et al. 2014) and their targets were predicted using psRNA target server (Dai et al. 2018) (http://plantgrn.noble.org/psRNATarget/) against the databases of sugarcane monoploid genome (Garsmeur et al. 2018) and assembled salinity transcriptome (Vignesh et al. 2021).

Validation of Small RNA-Seq and their targets

Four miRNAs such as sbi-miR159a, sbi-miR168, sbi-miR5568g-3p, sbi-miR167d and their targets such as Callose synthase 3, Oligopeptide transporter 3, Nucleotide pyrophosphatase/phosphodiesterase and ABRE-binding factor BZ-1 were chosen for validation of Small RNA-Seq (Supplementary Table 1). The total RNA isolation from the stressed and control root samples of IND 99-907 and Co 97010 was described in our previous studies (Vignesh et al. 2021) and normalized RNA samples were used for validation. The cDNA of mature miRNAs for stressed and control root samples of IND 99-907 and Co 97010 were synthesized using miScript II RT Kit (Qiagen) as per the manufacturer’s instructions. Briefly, a total of 2.0 µg total RNA was added with 4.0 µL of 5 × miScript HiSpec Buffer, 2 μL of 10 × miScript Nucleics Mix, 2 μL of miScript Reverse Transcriptase Mix and volume made up to 20.0 μL using RNase-free water. 5S RNA was used as an internal control for expressions analysis of miRNAs (Su et al. 2017) using miScript SYBR® Green PCR Kit (Qiagen) as per the manufacturer’s instructions. Briefly, 10 μL of 2 × QuantiTect SYBR Green PCR Master Mix, 2 μL of 10 × miScript Universal Primer, 2 Μl of 10 × miScript Primer Assay, and template 10–20 ng of cDNA of miRNA and volume made up to 20 μL using RNase free water. Quantitative real time PCR conditions followed were explained elsewhere (Dharshini et al. 2016; Narayan et al. 2021). The relative expression of miRNA was estimated using 2−ΔΔCT method (Su et al. 2017). The miRNA targets were validated as per the protocol described in our previous studies (Vignesh et al. 2021).

Results

Small RNA sequencing data and pre-processing

The small RNA sequencing of control and stressed root samples of IND 99-907 and Co 97010 was performed using Illumina HiSeq 2500 platform in 1 × 50 bp single-end format and labelled as 907C, 907S, 97010C and 97010S respectively. A total of 865,221,680 raw reads were generated, which includes 210,986,172, 214,385,900, 197,546,113 and 242,303,495 raw reads of 907C, 907S, 97010C and 97010S respectively. The high-quality reads with minimum Phred score ≥ 30 and length of 18–30 nucleotides after the removal of adopters, rRNA, tRNA, snRNA, snoRNA resulted in 58,716,698, 86,602,825, 57,530,483 and 66,998,728 clean reads from libraries of 907C, 907S, 97010C and 97010S respectively (Table 1). The length distribution for clean reads showed that 21 nucleotide reads were the most abundant (Fig. 1) and more than 8 million clean reads were length of 21 nucleotides. Among the four libraries, the maximum 21 nucleotide reads were observed in 907C samples and the lowest in 907S libraries.

Table 1.

Pre-processing of small RNA libraries of control and stresses samples of IND 99-907 and Co 97010

Type 907C 907S 97010C 97010S
Raw reads 210,986,172 214,385,900 197,546,113 242,303,495
Length < 18 65,870,723 117,605,365 67,905,970 80,108,269
Length > 30 159,385,521 179,557,250 135,127,012 179,496,276
tRNAs 133,147 8,738,922 239,337 687,129
rRNAs 2,154,031 770,157 2,227,623 2,241,794
snRNAs 130,069 148,874 191,261 263,420
snoRNAs 11,389 52,380 22,003 30,293
Cleaned reads (Length = 18–30) 58,716,698 86,602,825 57,530,483 66,998,728
Q30% 74.82% 92.35% 69.50% 75.25%
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Whereas, 907C and 907S denotes control and stress samples of E. arundinaceus respectively; 97010C and 97010S denotes control and stress samples of sugarcane cultivar Co 97010 respectively

Fig. 1.

Fig. 1

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Small RNA length distribution of four sequenced libraries

Identification and differential expression of salt responsive known miRNAs

Non-coding non-regulatory RNAs such as rRNAs tRNAs, and other RNA families were filtered out by aligning with SILVA (https://www.arb-silva.de/), GtRNAdb (http://gtrnadb.ucsc.edu/) and Rfam database (http://rfam.sanger.ac.uk/) respectively. The resultant clean reads were used for prediction of miRNA by aligning against the monocot miRNA data of miRBase database. A total of 362 known miRNAs belonging to 62 families in IND 99–907 and 353 miRNAs belonging to 63 families in Co 97010 were identified (Fig. 2). The miRNA families such as miR156, miR160, miR166, miR167, miR169, miR171, miR395, miR399, miR437 and miR5568 were the most abundant with more than ten members in both IND 99-907 and Co 97010 libraries. The miR169 family was the most abundant with 28 members in IND 99-907 and 32 members in Co 97010. Other miRNA families such as miR437 class were enriched with 25 members in IND 99-907 and 24 members in Co 97010, miR395 class with 24 members and miR166 class with 22 members in both genotypes. Only 1 miRNA was abundant in 11 and 9 miRNA families in IND 99-907 and Co 97010 respectively.

Fig. 2.

Fig. 2

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Distribution of miRNAs families in IND 99-907 and Co 97010

The differential expression analysis between control and stressed libraries resulted in identification of 221 miRNAs belonging to 48 families in IND 99-907 and 130 miRNAs belonging to 42 families in Co 97010 (p < 0.05). Among the 221 differentially expressed (DE) miRNA in IND 99-907, 101 miRNAs were upregulated and 100 miRNAs were downregulated (logFC > 1; logFC < −1). Similarly in Co 97010, 61 miRNAs were upregulated and 69 of them were downregulated. Among the differentially expressed miRNAs, 97 miRNAs were expressed in both genotypes, whereas, 124 and 33 miRNAs were uniquely expressed in IND 99-907 and Co 97010 respectively. Among unique DE miRNAs, 38 miRNAs were upregulated in both genotypes whereas 63 and 23 miRNAs were uniquely upregulated in IND 99-907 and Co 97010 respectively. Similarly, 50 and 19 miRNAs were uniquely downregulated in IND 99-907 and Co 97010 respectively, whereas 50 miRNAs were downregulated in both genotypes (Fig. 3). Among the 101 upregulated miRNAs in IND 99-907, 10 miRNAs were most abundant with expression fold change higher than 5.0 (logFC) and miR5568b-5p was abundant with log2FC = 6.597. Among 61 upregulated miRNAs in Co 97010, 37 miRNAs were abundant with expression fold-change greater than 5.0 and miR437r was abundant with log2FC = 7.771.

Fig. 3.

Fig. 3

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Venn diagram showing the unique and shared miRNAs between IND 99-907 and Co 97010; a total differentially expressed miRNAs, b upregulated miRNAs and c) downregulated miRNAs

Identification of salt responsive novel miRNAs in four libraries

The unaligned reads after the identification of known miRNA from the miRBase were mapped against sugarcane monoploid reference genome (Garsmeur et al. 2018) using miRDeep2 tool for identification of novel miRNAs. A total 6,032,125 (10.78%), 27,406,017 (35.60%), 7,999,544 (14.65%) and 15,178,810 (23.77%) reads were mapped against sugarcane monoploid genome. As per the standard miRNA annotation protocol (Meyers et al. 2008), miRNA with a copy number less than 10 were discarded (Yang et al. 2020) which resulted in identification of a total 194, 38, 184 and 96 novel miRNA from 907C, 907S, 97010C and 97010S libraries respectively. The novel miRNA identified were having 18–25 nucleotide length and distributed among all ten linkage groups (Fig. 4A).

Fig. 4.

Fig. 4

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A Linkage group-wise distribution of predicted novel miRNAs in sugarcane monoploid genome, B predicted hairpin structures of the precursor miRNAs and the mature sequences are marked in red, and C Venn diagram showing the distribution of novel miRNAs in the four libraries

A total of 194 novel miRNAs were identified in 907C library and among them, 87 were having ( +) strand and 98 were having (−) strand. Similarly, 16, 90 and 45 novel miRNAs were present in the ( +) strand and 22, 97 and 53 novel miRNAs were present in the (−) strands of 907S, 97010C and 97010S libraries respectively. With regards to linkage group-wise distribution, the highest number of novel miRNA were located on LG-1, a total of 39 and 19 novel miRNA in control libraries whereas 8 and 32 novel miRNA in stressed libraries were located on LG1 respectively. In the library 907S, the chromosomal distribution was < 10 with no novel miRNAs predicted in chromosomes 5 and 8, whereas, only 1 novel miRNA was observed in chromosome 9. A total of 10, 6, 7 and 4 novel miRNAs had mature read counts greater than 1000 in the four libraries of 907C, 907S, 97010C and 97010S respectively. With regards to read counts, novel miRNAs viz., Sh09_38450, Sh03_9706, Sh09_41462 and Sh01_6609 were recorded the highest number of read counts of 13,066, 10,129, 15,460 and 13,108 reads in the libraries 907C, 907S, 97010C and 97010S respectively. In IND 99-907, only three novel miRNAs were abundant in both control and stressed libraries. A total of 28 novel miRNAs were abundant in both libraries of Co 97010. With regards to cross comparison of novel miRNA between IND 99-907 and Co 97010, novel miRNA (149) were more abundant in the control libraries as compared to 139 novel miRNAs in stressed libraries (Fig. 4C). All of the predicted novel miRNAs had significant randfold p values with no similarity domains in Rfam database. Both precursor (pre-miRNA) and star (miRNA*) sequences were present for predicted novel miRNAs supporting the miRNA biogenesis. The structures of the novel miRNAs were predicted and few of the representative structures of novel miRNAs showing mature miRNAs located on the stem arms of the hairpin secondary structures are presented in Fig. 4B. Moreover, the minimum free energies (MFE) of the precursor sequences were calculated and it ranged from − 68.70 to − 7.00 kcal/mol, − 44.70 to − 8.70 kcal/mol, − 56.40 to − 8.10 kcal/mol and − 68.90 to − 2.60 kcal/mol with an average length of 63, 59, 63 and 62 nucleotide for 907C, 907S, 97010C and 97010S libraries respectively.

Target gene prediction of miRNA and their annotation

To identify the target genes of miRNA, differentially expressed miRNA from IND 99-907 and Co 97010 (p < 0.05) were targeted against the monoploid mosaic sugarcane genome, our de novo salt transcriptome assemblies and differentially expressed genes (DEGs) (p < 0.05) under stressed and control libraries of IND 99-907 and Co 97010. The gene annotation for the DEGs was obtained by performing a blastn search against the annotated cDNA sequences of the sugarcane monoploid genome. The miRNA target prediction against the monoploid mosaic sugarcane genome resulted in identification of 12,693 gene targets for 221 DE miRNA in IND 99-907 and 7982 gene targets for 130 DE miRNA in Co 97010. In the case of our salt transcriptome assembly (Vignesh et al. 2021), a total of 15,031 and 12,152 miRNA targets were predicted for DE miRNA of IND 99-907 and Co 97010 respectively. Further, to study the miRNA-mRNA based on their expression profiles, DE miRNAs were specifically targeted against 7731 and 6159 DEGs of IND 99-907 and Co 97010 respectively and a total of 4378 and 2744 target genes or DEGs were identified in IND 99-907 and Co 97010 respectively. The predicted interactions between miRNAs and their targets showed that same miRNAs targeting multiple genes as well as the same gene targeted by multiple miRNAs.

Expression patterns of miRNA and their interaction with targets

To analyse the salinity stress response of two contrasting genotypes IND 99-907 and Co 97010, both miRNA and their target expression analysis was studied decipher their interactions. The expression patterns between miRNA and their targets were classified into three categories viz., type I interaction: both miRNA and their targets were upregulated, type II Interaction: both miRNA and their targets were down-regulated, type III interaction: miRNA was upregulated and their targets were down regulated, and type IV interactions: miRNA was down regulated and its targets were upregulated (Aravind et al. 2017). A total of 594, 222, 225 and 402 known miRNA in IND 99-907, and 190, 280, 188 and 245 known miRNA in Co 97010 were interacted with their targets in type I, II, III and type IV pattern respectively.

Gene ontology (GO) enrichment analysis for miRNA targets

The GO enrichment analysis of the miRNA targets were carried out to elucidate the functional roles of miRNA and their targets for salt responsive biological, molecular and cellular function in IND 99-907 and Co 97010. We identified 24, 12 and 14 enriched GO terms (FDR < 0.05) in three categories biological process, molecular function and cellular component respectively (Fig. 5). The salt responsive biological processes such as transport (GO:0006810), signal transduction (GO:0007165), response to abiotic stimulus (GO:0009628), response to stress (GO:0006950) and cellular developmental processes (GO:0048869) GO terms were significantly enriched under salinity stress. In the salt responsive molecular function category, binding (GO:0005488), transporter activity (GO:0005215) and hydrolase activity (GO:0016787) GO terms were significantly enriched and intracellular part (GO:0044424) and cytoplasmic part (GO:0044444) of cellular components GO terms were significantly enriched under salinity stress.

Fig. 5.

Fig. 5

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Overview of GO annotations of potential target genes of miRNAs derived from the transcriptome studies of E. arundinaceus accession IND 99-907 and salinity sensitive sugarcane cultivar Co 97010. The data represents three GO categories namely biological process, molecular function and cellular component

Validation of small RNA-Seq and their targets

We have randomly selected four miRNA (sbi-miR159a, sbi-miR168, sbi-miR5568g-3p, sbi-miR167d) and their targets (Callose synthase 3, Oligopeptide transporter 3, Nucleotide pyrophosphatase/phosphodiesterase and ABRE-binding factor BZ-1) for validation of Small RNA-Seq (Supplementary Table 1) in E. arundinaceus accession IND 99-907 and salinity sensitive sugarcane cultivar Co 97010. The differential expression pattern of miRNA in Small RNA-Seq and their targets in Transcriptome studies were comparable with expression pattern in RT-PCR (Fig. 6).

Fig. 6.

Fig. 6

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Validation of randomly selected four miRNA and their targets in salt tolerant E. arundinaceus accession IND 99-907 and salinity sensitive sugarcane cultivar Co 97010

Discussion

The modern sugarcane varieties are salt sensitive and salinity stress causes the significant reduction in crop growth and biomass accumulation in sugarcane (Saxena et al. 2010; Uwatoko et al. 2011; Zhao et al. 2020; Mohanan et al. 2021). E. arundinaceus is a wild relative of sugarcane with high biomass production potential and harbours many genes for agronomic, biotic and abiotic stresses (Uwatoko et al. 2011; Augustine et al. 2015a,b). Though the miRNA studies were reported for various physiological, biotic and abiotic stresses in sugarcane such as aerenchyma cell formation (Queiroz de Pinho Tavares et al. 2020), cold stress (Yuan et al. 2020), aluminium stress (da Silva et al. 2019), smut disease (Su et al. 2017) and bacilliform virus (Ashraf et al. 2020), the role of miRNA in regulating of salt responsive mechanism in sugarcane is poorly understood. The abundance of miRNA and their role in regulation for growth, developmental, physiological, biotic and abiotic stress tolerances were well studied and reviewed in many crops (Gao et al. 2016; Bai et al. 2018; Cui et al. 2020; Waheed and Zeng 2020). The genome-wide comparative profiling of salt responsive miRNA helps to uncover the possible role of miRNA in conferring the salt stress tolerance in salt tolerant E. arundinaceus genotype IND 99-907 and salt-sensitive sugarcane genotype Co 97010.

Next generation sequencing approaches are frequently used for identification of genome-wide identification of miRNA and their targets in many crops like rice (Chopperla et al. 2020), wheat (Zhou et al. 2019; Hou et al. 2020), maize (Li et al. 2016), sorghum (Katiyar et al. 2015), foxtail millet (Yi et al. 2013) and many other crops. The reduction in cost of sequencing and recent advances in bioinformatic analysis of NGS data (Muir et al. 2016; Li et al. 2019) paved the way for genome-wide discovery of miRNA in non-model crops like citrus (Xie et al. 2017), Dimocarpus longan (Xu et al. 2020), Camellia sinensis (Jeyaraj et al. 2017), Osmanthus fragrans (Shi et al. 2021), Achyranthes bidentata (Yang et al. 2021a) and many other species. In our studies, we investigated the comparative miRNA profiling to identify the salt responsive conserved and novel miRNA associated with salt tolerance in IND 99-907 and Co 97010. We have generated 58.72, 86.60, 57.53 and 66.99 million of clean reads from the control and stressed samples of IND 99-907 and Co 97010 respectively and clean reads generated in our studies were higher than previous reports in sugarcane (da Silva et al. 2017; Su et al. 2017; Yang et al. 2017) and recent reports in other plant species (Chopperla et al. 2020; Hou et al. 2020; Yang et al. 2020, 2021a, b; Li et al. 2021a; Shi et al. 2021). The sRNA distribution was ranged from 18 to 25 nucleotide length and 21 nucleotide sRNAs were more abundant in both stressed and control libraries of IND 99-907 and Co 97010. This is in concurrence with abundance of 21 nucleotide sRNA under salinity stress in other crops (Sun et al. 2015; Xie et al. 2017; Yang et al. 2020).

The reference genome sequences are lacking in E. arundinaceus and only the monoploid mosaic genome of sugarcane is available in public databases (Garsmeur et al. 2018) made us to use of our salinity root transcriptome data for prediction of comparative salt responsive miRNA targets in IND 99-907 and Co 97010. Besides, the transcriptome-based miRNA predictions are more informative in uncovering the regulatory network of miRNA in non-model crops which are lacking of the complete genome sequences (Xie et al. 2017; Chen et al. 2019; Zhou et al. 2020; Yang et al. 2021b) justifying the use integrated miRNA-transcriptome profiling to decipher the regulatory network of miRNA and their targets for salinity tolerance. Small RNAs were mapped on to the mature miRNA data on to the sorghum miRNA in miRbase, we identified 362 known miRNAs belonging to 62 families in IND 99-907 and 353 miRNAs belonging to 63 families in Co 97010. The most of the known miRNA identified in IND 99-907 and Co 97010 were reported in other species (Chavez Montes et al. 2014) reaffirms to confidence in sRNA data generated in our studies. We have adopted statistically stringent steps in identification of novel miRNA by aligning against sugarcane monoploid genome (Garsmeur et al. 2018) and minimum threshold of MFEI was − 0.85 (− 68.70 to − 7.00 kcal/mol, − 44.70 to − 8.70 kcal/mol, − 56.40 to − 8.10 kcal/mol and − 68.90 to − 2.60 kcal/mol with an average length of 63, 59, 63 and 62 nt for the four libraries 907C, 907S, 97010C and 97010S respectively) precisely resolve the mature novel miRNA identifications. The stringent bioinformatic and reliable methodologies adopted in our studies in identification of known and novel miRNA helps to uncover the regulatory role of miRNA for salinity stress tolerance in IND 99-907 and Co 97010.

Several studies have demonstrated the role of miRNA in regulating the physiological and agronomic traits and tolerances to biotic and abiotic stresses in sugarcane such as abundance of 100 known miRNAs families for aluminium stress (da Silva et al. 2019), 8 known and 31 novel miRNAs for aerenchyma formation in roots, about 260 known miRNAs for smut disease (Su et al. 2017) and 200 known miRNAs for cold acclimation (Yang et al. 2017). In our studies, we observed 117 known miRNA belongs to 38 families in Co 97010 and 146 known miRNAs belong to 44 families in IND 99-907. Overall, total of 156 known miRNAs belongs to 48 miRNA families were abundant in both genotypes under salinity stress. Most of the known miRNAs were abundant in both genotypes in our studies indicating the complex interactions between known miRNA and their targets. Notably, sbi-miR5568b-5p showed the highest differential upregulation in IND 99-907 and that of sbi-miR5568g-5p upregulated differentially expressed in Co 97010. The families of miR5568 are highly conserved in both monocots and dicots (Chavez Montes et al. 2014), their abundance was reported for salinity stress in wild emmer wheat (Feng et al. 2017) and known to regulate the several abiotic stress tolerance through modulating the target genes such as HSP20 (Nagaraju et al. 2020) and NF-Y genes in sorghum (Maheshwari et al. 2019). In our studies, five miRNAs belonging to miR5568 family were upregulated and positively regulating the 11 unigenes and negatively regulating the two unigenes in IND 99-907. In IND 99-907, sbi-miR5568d-3p upregulated (4.12 logFC) has targets in upregulating the unigenes such as Protein HUA2-LIKE 3 (20.21 log2FC), callose synthase 3-like (8.86 log2FC) and tetratricopeptide repeat protein 38 isoform X2 (7.93 log2FC) and these targets are associated with biosynthesis of nuclear factor, biosynthesis of callose and abscisic acid signalling (Rosado et al. 2006; Vatén et al. 2011; Jali et al. 2014), whereas in Co 97010, seven miRNA belongs to seven different families have targets in 57 differentially expressed salt responsive unigenes.

The differential expression of miRNA and regulation of their target genes under salinity stress is reported in many crops (Ding et al. 2009; Wang et al. 2019c; Kouhi et al. 2020; Parmar et al. 2020) including sugarcane (Ferreira et al. 2012; Carnavale Bottino et al. 2013; Yang et al. 2017; Silva et al. 2021). In our studies, a total of 221 miRNA belonging to 48 families in IND 99-907 and 130 miRNA belonging to 42 families in Co 97010 were identified. Among them, miRNA families such as sbi-miR156, sbi-miR319, sbi-miR397, sbi-miR1432, sbi-miR2118, sbi-miR5564, sbi-miR6220 in IND 99-907 and sbi-miR390, sbi-miR5381, sbi-miR5567, sbi-miR6226, sbi-miR6235 in Co 97010 were uniquely and differentially expressed in our studies. The uniquely differentially expressed miRNA families of IND 99-907 characterized for their role in regulating the various biotic and abiotic stresses such as miR156 and sbi-miR319 for salt tolerance (Zhou et al. 2013; Kouhi et al. 2020), miR1432, miR2118 and miR6220 for drought tolerance (Wu et al. 2015; Nagaraju et al. 2019; Li et al. 2021b); miR397 and miR5564 for herbicide tolerance, nutrient deficiency and biomass accumulation in many crops (Pan et al. 2017; Patel et al. 2019; Zhu et al. 2021).

Many known miRNA regulating various biotic and abiotic stresses are also found in our study. The miRNA miR5568 was reported to upregulated under salinity stress (Feng et al. 2017) and its family members viz., sbi-miR5568b-5p, sbi-miR5568e-3p and sbi-miR5568g-3p were upregulated only in IND 99-907, whereas sbi-miR5568g-5p, sbi-miR5568e-5p and sbi-miR5568f-3p were expressed only in Co 97010. The monocot specific miR6220 (Chavez Montes et al. 2014) family member sbi-miR6220-5p was upregulated in IND 99-907 and did not differentially expressed in Co 97010. Besides, miR160 regulates the leaf development and conferring tolerance to drought (Yang et al. 2019), In this study, sbi-miR160b was upregulated in salinity tolerant clone IND 99-907 and sbi-miR160f was down regulated in salinity sensitive genotype Co 97010. MiR396 regulating the vernalization and triggering the immune response to biotic stresses, its family member, miR396a was upregulated only in IND 99–907, miR396b was upregulated in IND 99-907 and down regulated in Co 97010. Similarly, 221 and 130 known miRNA were differentially expressed in IND 99-907 and Co 97010 provides insight into the role of miRNA and their targets conferring salt tolerance in E. arundinaceus.

The sequencing of vascular plants revealed that miR6220 family was monocot specific, less conserved across plant systems, 21 and 24 nucleotide types were most abundant and their abundance ranged 1–10 per million reads (Chavez Montes et al. 2014). The abundance of miR6220 was reported for water stress in S. narenga (Liu et al. 2018), drought and salt tolerance in sorghum (Katiyar et al. 2015; Nagaraju et al. 2019), salt stress in wheat (Feng et al. 2017) and phosphate deficiency in maize (Gupta et al. 2017). In our study, sbi-miR6220-3p were abundant in both IND 99-907 and Co 97010, whereas, sbi-miR6220-5p expressed only in IND 99-907. The predicted targets for sbi-miR6220-5p in our salt transcriptome data (Vignesh et al. 2021) were phosphoglucan phosphatase DSP4 (ERI-C_DN2351_c0_g1_i4.p1), callose synthase 3-like (ERI-S_DN0_c1_g3_i3.p1) and chromatin modification-related protein EAF1 B isoform X1 (ERI-S_DN1267_c0_g1_i27.p1). The abundance of miR6220 under salt tolerance was reported in Aegilops tauschii (Feng et al. 2017) and it is a part of histone acetyltransferase complex of EF1B associated with DNA repair mechanism (Auger et al. 2008). Callose synthase 3-like associated biosynthesis of callose and its accumulation in plasmodesmata regulate the movement of transcriptional factors and miRNA (Vatén et al. 2011) and phosphoglucan phosphatase DSP4 associated with starch degradation during the cold acclimation (Berrocal-Lobo et al. 2011).

The miR6232 abundant in plant system (Nithin et al. 2017) and are regulating the tolerances to biotic and abiotic stresses in plants. It has target genes for abiotic stress tolerance such as HSP70 (Buyuk et al. 2016) and sorghum nuclear factor Y family (Maheshwari et al. 2019). In our study, sbi-miR6232b-5p was abundant under stressed condition and sbi-miR6232a-5p under controlled condition IND 99-907 and its expression was not found in Co 97010. The target identification from our transcriptome data has showed that miR6232b-5p has targets in 13 differentially expressed unigenes in stressed sample of IND 99-907 and eight unigenes in control sample of IND 99-907. The differentially expressed targets and miRNA analysis showed that miR6232b-5p binds to 3′ UTR regions of pre-mRNA splicing factor CWC22 (ERI-S_DN5828_c0_g1_i99.p2, upregulated by 19.47 logFC) and regulates the mRNA processing and confer tolerance to abiotic stress tolerance (Steckelberg et al. 2012; Han et al. 2017; Zhu et al. 2020). The miR6232b-5p interacts with 3′ UTR and coding regions of the chromatin modification protein EAF1-B (ERI-S_DN1267_c0_g1_i27.p1; upregulated by 20.42 logFC), thereby regulates the DNA repair mechanism during abiotic stress tolerance (Auger et al. 2008; Feng et al. 2017). The FAB1D phosphatidyl inositol kinase has targets for miR6232b-5p in the coding regions and FAB1D (ERI-S_DN5272_c0_g1_i5.p1, upregulated by 9.38 logFC) associated with synthesis of phospholipids from phosphatidylinositol and regulates various physiological responses in development, biotic and abiotic stress responses (Serrazina et al. 2014; Hirano and Sato 2019; Mueller-Roeber and Pical 2002). The TRRAP (ERI-S_DN4770_c0_g1_i40.p1, upregulated by 9.19 logFC) regulates the replication, transcription and DNA repair (McMahon et al. 1998; Murr et al. 2007), has target for miR6232-5p at 3′ UTR region. Besides, other upregulated targets for miR6232b-5p are MAPKKK (ERI-S_DN2233_c0_g1_i38.p1, upregulated by 7.66 logFC) associated with ABA signal transduction has targets at 3′ UTR, aldehyde oxidase isoforms (ERI-S_DN16627_c1_g1_i1.p1, upregulated by 3.11 logFC) regulated the ABA biosynthesis and secondary metabolites (Omarov et al. 2003; Colasuonno et al. 2017; Srivastava et al. 2017) has targets at coding regions, plasma membrane type calcium transporting ATPase 5 (ERI-S_DN2288_c0_g1_i14.p1, upregulated by 1.92 logFC) regulates calcium mediated signalling during abiotic stresses (Huda et al. 2013) has target at coding regions. Sucrose Transport Protein SUT4 and protein RDM16 regulates the DNA methylation and cytokinin signalling (Sharma et al. 2017; Lv et al. 2021) has targets at coding regions for miR6232b-5p.

Conclusion

We performed the comparative transcriptome and miRNA profiling analysis between salinity stressed and control root samples of salt tolerant E. arundinacesus clone IND 99-907 and salt-sensitive sugarcane genotype Co 97010. Differentially expressed miRNA used to predict their target mRNAs using transcriptome data. We identified differentially expressed miRNA in both genotypes and their uniquely expression in control and stressed samples in salt tolerant and salt-sensitive sugarcane genotypes. The gene ontology analysis of miRNA targets predicted from transcriptome data revealed that many salt responsive processes such as transport, signal transduction, response to abiotic stimulus, response to stress and cellular developmental processes GO terms were significantly enriched under salinity stress. Many target genes related to biotic and abiotic stresses were differentially expressed in our studies. Further studies are required to overexpress miRNA and their targets for utilization in the crop improvement programmes.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (XLSX 97 KB) (96.8KB, xlsx)

Acknowledgements

The authors are respectfully thanking and acknowledging the financial support from ‘Science and Engineering Research Board, Department of Science and Technology (DST-SERB)’ under Early Career Research Award (File No.: DST/SERB/ECR/2017/000349). We are also thankful to AgriGenome Labs Pvt Ltd, Cochin, India for transcriptome and small RNA sequencing work. We thankfully acknowledge the ICAR-Sugarcane Breeding Institute, Coimbatore for kind support for the study.

Author contributions

PV & CM: equal contributions to research work or shared first co-authors and bioinformatics data analysis. CM, CA: project formulation and execution of field work. PV, CM, CA: project formulation, bioinformatics flow of data analysis, designed the experiment for performing genome assembly and differential gene expression. KS, CM, CA: execution of field work. CM: uploaded the raw data of small RNA Seq to NCBI website. CM, VS, HKM, KS: RNA isolation and performed qRT-PCR validation. PS, CM: prepared all tables, figures and wrote the manuscript. CM, CA: overall supervised the experiment. All authors read the manuscript and approved.

Funding

This research was supported by the financial grant received from ‘Science and Engineering Research Board, Department of Science and Technology (DST-SERB)’ under the “Early Career Research Award (File No.: DST/SERB/ECR/2017/000349)”.

Data availability

The Illumina raw reads of samples of transcriptome were deposited in NCBI BIO PROJECT with accession number PRJNA716503 and raw data of Small RNA-Seq of stressed and control samples of IND 99-907 and Co 97010 were deposited in NCBI BIO PROJECT with accession number PRJNA888452, PRJNA888470, PRJNA889536 and PRJNA889539 respectively.

Declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethical approval

Experimental material involves the research between sugarcane wild relative Erianthus arundinaceus and modern sugarcane genotype (Saccharum spp) is in compliance with institutional, national, and international guidelines and legislation.

Footnotes

$Palanisamy Vignesh and Channappa Mahadevaiah shared first authors and authors with equal contributions.

Contributor Information

Channappa Mahadevaiah, Email: C.Mahadevaiah@icar.gov.in.

Chinnaswamy Appunu, Email: cappunu@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 97 KB) (96.8KB, xlsx)

Data Availability Statement

The Illumina raw reads of samples of transcriptome were deposited in NCBI BIO PROJECT with accession number PRJNA716503 and raw data of Small RNA-Seq of stressed and control samples of IND 99-907 and Co 97010 were deposited in NCBI BIO PROJECT with accession number PRJNA888452, PRJNA888470, PRJNA889536 and PRJNA889539 respectively.

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