Background

India is rich in spices and has been regarded as the “Spice Bowl of the World”. Ginger (Zingiber officinale Rosc.), the major spice and medicinal crop, is cultivated mainly in tropical and subtropical regions of India. The rhizomes contain several bioactive compounds used as a flavouring agent, herbal medicine and is also employed in the perfume industry [1, 2]. India is the major producer of ginger, accounting for 31% of the world production [3]. Several reports have been published regarding the crop loss due to bacterial wilt caused by Ralstonia solanacearum [2]. Bacterial wilt is considered as a major disease especially in regions with warm climates [4]. India has crossed yield losses of more than 50% due to this infection [5]. Around the world, the R. solanacearum, a soil-borne bacterium, causes bacterial wilt in many plant families such as tomato, potato, pepper, peanut, banana and eggplant [6]. This gram-negative bacterium infects plants through axils of secondary roots, which later invades cortex and then colonizes in the xylem vessels which causes wilt symptoms and death [7].

Many recent studies have increasingly concentrated on host plant resistance than traditional chemical treatments, which seems to be more powerful and economical in controlling bacterial wilt infection [8]. Several enhanced resistance varieties of potato, tomato, peanut, eggplant, and banana have been successfully generated by transforming the resistant gene to the plant [7, 9]. Research has been carried out to identify the resistance source to Ralstonia induced bacterial wilt in ginger. In a study, the ICAR-Indian Institute of Spices Research, Kozhikode, Kerala, reported mango ginger (Curcuma amada Roxb.) from the ginger family Zingiberaceae, with a high level of resistance against bacterial wilt. Comparative transcriptomics of ginger and mango ginger during bacterial wilt has resulted in identifying a considerable number of candidate genes [10].

Earlier reports states that microRNAs (miRNAs) are hypersensitive to diverse physiological processes such as abiotic or biotic stress [11, 12]. miRNAs which are approximately 21-nucleotide noncoding RNAs, serve a crucial role in post-transcriptional gene regulation by degrading target mRNAs in plants. Previous researchers have identified several plant miRNAs and their targets, which are related to biotic stress. This supports that miRNAs are crucial to the stress response of plants [13]. Thus far, the miRNA expression profiles of ginger under bacterial stress conditions have rarely been reported. Efforts to identify bacterial wilt responsive miRNAs and determine their expression patterns would improve our understanding of their stress adaptation functions.

Several researchers reported that pathogen attack in plants induce numerous miRNAs and later control and contribute towards the reprogramming of gene expression [12,13,14,15,16,17,18,19]. It was reported that during wheat-stripe rust infection, several variants of known miRNAs and 163 candidate novel wheat miRNAs were differentially expressed [12]. Much research has been carried out to overexpress significant miRNAs to enhance disease resistance in susceptible plants. Overexpressing miR319b in rice found to be a positive regulator of the rice defence response against the blast disease [14]. Differential expression of miRNAs was observed in wheat and barley after infection with powdery mildew [15, 16]. The introduction of artificial miRNA has been carried out in several plants against viral infection, such as wheat, maize, tomato, and grape wine [17,18,19].

This study employed already available transcriptome to investigate and elucidate a detailed and in-depth characterization of miRTGs and their expression in ginger (susceptible) and mango ginger (resistant) transcriptome during R. solanacearum infection. We identified miRTGs and their known miRNAs involved in defence-related, plant-pathogen interaction and disease resistance. Our results provided valuable information to reveal the molecular mechanism of miRNAs and their targets in resisting mango ginger against R. solanacearum infection.

Results

Identification of miRTGs

Already available ginger and mango ginger RNA-Seq data from our lab was utilized to reconstruct transcriptome de novo. A total of 80,496,326 and 66,561,960 raw reads, accounting approximately for mango ginger and ginger, respectively, were generated (Table 1). The mango ginger assembly was represented by 307,952 contigs with an average of 845.32 bps, while the ginger assembly was represented by 303,878 contigs with an average contig size of 692.32 bps. The N50 for mango ginger and ginger with de novo assemblies were 1256 and 1005, respectively. These assembled contigs were further used to identify known miRTGs. After carefully considering the alignment results, we located a total of 4736 and 4485 DEmiRTGs (significant p-value <  = 0.05 and having functional annotation) in ginger and mango ginger, respectively (Fig. 1).

Table 1 Assembly summary of transcripts of mango ginger and ginger
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Fig. 1
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Numbers of differentially expressed total miRTGs in response to bacterial wilt in ginger and mango ginger

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Identification of miRNAs from miRTGs

The ‘psRNATarget’ server was utilized to map miRNAs from miRTGs. Each miRTG provided a varying number of miRNAs. In ginger, maximum miRNAs (1193) were identified for the target, miRTG UBC24_ARATH. Among ginger miRTGs, 271 did not show any corresponding miRNAs. In mango ginger, miRTG NFYA7_ARATH had the maximum number of miRNAs (320). No miRNAs were identified for 315 mango ginger miRTGs. Each miRTGs had targets for several miRNAs and vice versa and they combined to form a complex network (Fig. 2; Supplementary data 1).

Fig. 2
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Regulation network constructed with selected miRTGs and corresponding miRNAs. Yellow indicates miRTGs and nodes indicates corresponding miRNAs

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Differential expression of miRTGs

We identified 4736 differentially expressed miRTGs in ginger (susceptible), out of which 2300 were upregulated and 2436 were downregulated. In mango ginger (resistant), out of 4485 DEmiRTGs, 2258 were upregulated, and 2227 were downregulated (Fig. 1). Among the total 9921 DEmiRTGs in response to R. solanacearum, 2105 were common in ginger and mango ginger. The common DEmiRTGs revealed, 965 upregulated and 1140 downregulated in ginger and 776 upregulated, and 1329 downregulated in mango ginger (Fig. 3). When compared with ginger, 2337 miRTGs were expressed only in mango ginger. Moreover, we also observed 215 miRTGs upregulated in resistant mango ginger but downregulated in susceptible ginger (Fig. 4). Nine miRTGs were selected based on their function and fold change from the common miRTGs differentially expressed in both plants. Primers were designed for each target genes and its miRNAs (Tables 2 and 3). The real-time PCR analysis of these miRTGs genes and their miRNAs revealed their differential expression among the two ginger species.

Fig. 3
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Numbers of differentially expressed common miRTGs in response to bacterial wilt in ginger and mango ginger

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Fig. 4
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Heatmap showing the expression levels of the common DEmiRTGs of Zo (ginger) versus Ca (mango ginger). The color code indicates relative abundance, ranging from red (low abundance) to green (high abundance)

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Table 2 Primer designed for target genes to perform qPCR experiments
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Table 3 Primers designed to perform qPCR experiments with the shortlisted nine miRNAs
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GO and KEGG pathways of DEmiRTGs

Gene ontology (GO) analysis of DEmiRTGs was performed and grouped into three categories: biological process, cellular component, and molecular function. In the biological function category “defence response” represented the top term in ginger and mango ginger. The most abundant terms in cellular component category were “membrane” in ginger and mango ginger. In the molecular function, the GO term “ATP binding” was enriched in ginger and mango ginger (Fig. 5).

Fig. 5
figure 5

GO analysis of DEmiRTGs in response to bacterial wilt in ginger and mango ginger

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The GO analysis of upregulated DEmiRTGs expressed exclusively in mango ginger showed maximum enrichment in defence response (120), out of which 29 were related to “defence response in bacterium”. Among the 2105 common miRTGs, 45 and 71 were “defence response” GO terms upregulated in ginger and mango ginger, respectively. There were 213 miRTGs which were upregulated in mango ginger but downregulated in ginger. Among them 23 GO terms were related to “defense response”, which was the highest among all GO terms. Also, 10 GO terms were related to “defence response in bacterium” (Table 4).

Table 4 Differential expressed defense related miRTGs in response to Ralstonia solanacearum
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The top 10 enriched pathways regulated by Ralstoina are summarized in Fig. 6. The most enriched pathways were related to protein and amino acid pathways such as “protein modification” and “protein ubiquitination”. Interestingly, among the top 10 enriched pathways, we observed that the number of DEmiRTGs in mango ginger was more than ginger in the carbohydrate degradation and glycolysis pathways. Thus, indicating the difference and significance of carbohydrate metabolism in giving resistance against bacterial wilt.

Fig. 6
figure 6

KEGG pathway analysis of DEmiRTGs in response to bacterial wilt in ginger and mango ginger

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Validation and correlation analysis of miRTGs expression profiles and their miRNAs

To specify the roles of miRNAs in response to bacterial wilt, the expression profiles of nine miRNAs and miRTGs common in both ginger species were analysed.

The qPCR analysis validated nine selected miRNA target genes (4CLL1, ABCC5, PRT6, RPS2, FRS6, AAP4, WRKY19, ABCG11 and DNAJ1) and demonstrated their differential expression in leaf and rhizome tissues of both ginger and mango ginger during bacterial wilt infection. Among the nine miRTGs, seven were upregulated in leaves and eight in rhizome tissues of ginger. In mango ginger miRTGs, six in leaves and seven in rhizome were upregulated. Among the miRTGs, four (4CLL1, ABCC5, PRT6 and RPS2) showed upregulation in both the tissues of ginger and mango ginger. The miRTGs, FRS6 and WRKY19, were upregulated in ginger but downregulated in mango ginger, whereas AAP4 showed downregulation in ginger and upregulation in mango ginger. The ABCG11 was upregulated in all three samples except ginger leaves. In DNAJ1, an upregulation was observed in all samples except mango ginger leaves (Table 5 and Fig. 7).

Table 5 List of upregulated and downregulated genes
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Fig. 7
figure 7

Normalized relative gene expression levels of nine miRNAs and targets in ginger and mango ginger leaf and rhizome tissues at different time intervals post inoculation with R. solanacearum.A 4CLL1 & ath-miR1886, B ABCC5 & mtr-miR5261, C PRT6 & osa-miR169, D RPS2 & gra-miR482, E FRS6 & ppt-miR1223, F AAP4 & mtr-miR164, G WRKY19 & aly-miR398, H ABCG11 & gma-miR4415 and I DNAJ1 & ath-miR167. Values are the means ± standard deviation of three independent experiments involving three replicates. Values superscripted with the different letters are significantly different based to Tukey’s HSD at P ≤ 0.05

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Stem-loop PCR was performed to analyse the expression of nine miRNAs (ath-miR1886, mtr-miR5261, osa-miR169, gra-miR482, ppt-miR1223, mtr-miR164, aly-miR398, gma-miR4415 and ath-miR167). Among them, mtr-miR164 and ppt-miR1223 showed upregulation in both the tissues of ginger and mango ginger. However, ppt-miR1223 downregulated in ginger rhizome after 8 h post-inoculation. The miRNAs, gra-miR482 and ath-miR167 upregulated in both tissues of mango ginger but downregulated in ginger in all the time intervals. Ath-miR1886 and aly-miR398 were upregulated only in mango ginger rhizome post inoculation (Fig. 7).

The correlation analysis was carried out using the Pearson correlation coefficient to understand the influence of miRNAs and their targets. The results of miRNAs showing Pearson correlation coefficient with its corresponding miRNA target of more than 0.9 is given in Table 6. However, the expression profiles of all the miRNAs were not perfectly negative to its corresponding targets.

Table 6 Correlation analysis of miRNAs expression profiles and their target genes in response to Ralstonia solanacearum
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Discussion

Strategies to reveal the disease-resistant mechanism of mango ginger

Ginger, a herbaceous tropical perennial crop, belongs to the family Zingiberaceae [20]. Among the diseases that affect ginger, bacterial wilt caused by R. solanacerum is one of the major production constraints in several regions of the world [21]. Even though there are several released varieties in ginger, none of them is resistant to R. solanacearum infection due to lack of genetic variability. Among the primary and secondary gene pool, the mango ginger (C. amada) exhibited immune response to R. solanacearum [22]. Initially, PR5 genes in ginger and mango ginger were cloned, and their expression was analyzed to understand their role in disease resistance [23]. Later, number of candidate genes were identified based on transcriptome-wide sequencing of ginger and mango ginger under bacterial wilt [10]. Moreover, a web source was generated, which provides public access to the ginger and mango ginger transcriptome database. Here, we employed the same RNA-Seq data to identify the miRNAs and their targets in two ginger species upon R. solanacearum infection. Also, to determine the differential expression and regulation of miRNAs and their targets. The study is expected to deliver several insights in understanding the miRNA regulatory network in ginger and mango ginger against R. solanacearum.

Differential expression and functional annotation of DEmiRTGs

A total of 4736 and 4485 DEmiRTGs were identified in ginger and mango ginger in response to R. solanacearum (Fig. 1). Functional annotation results showed that the term “defence response’’ had higher enrichment in mango ginger than ginger. Among the upregulated common miRTGs and miRTGs exclusively expressed in mango ginger, maximum enrichment was observed in the “defence response”. Moreover, there were 215 miRTGs which was upregulated in mango ginger but downregulated in ginger. Among them 23 GO terms were related to “defence response”. Also “defence response towards bacterium” was the major GO term in all cases. The GO analysis clearly revealed the activation and higher expression of several defence-related proteins, especially defence response proteins towards bacterium in resistant ginger. KEGG pathway analysis showed that carbohydrate metabolism terms in mango ginger were higher when compared to ginger. It was previously reported that carbohydrate metabolism was disturbed during R. solanacearum infection in tomato [24]. A well-established activation of carbohydrate metabolism pathway genes may be one reason for bacterial wilt resistance in mango ginger.

Validation of differentially expressed selected miRTGs

This study mainly focused on the identification miRTGs, and their potential miRNAs involved in regulating bacterial wilt defence reactions. We have selected nine miRTGs and validated their expression. These selected miRTGs were previously reported to have a significant role in plant-pathogen interaction during biotic stress.

4CLL (4-coumarate-CoA ligase) is considered an essential component in the phenylpropanoid pathway [25]. It primarily participates in the biosynthesis of p-coumaroyl-CoA, a precursor biosynthesis of several plant metabolites, including coumarin [26]. Coumarin accumulation is considered one of the most critical responses in infection with various viruses, fungi, and pathogens [27, 28]. The role of coumarin in defence response against R. pseudosolanacearum infection in tobacco has been reported previously [29]. In our study, the expression of 4-coumarate-CoA ligase (4CLL1) was upregulated in ginger and mango ginger during bacterial wilt infection. Moreover, the expression levels were higher in mango ginger compared to ginger in both tissues. This clearly shows the role of this gene during bacterial wilt (Fig. 7A).

Though initially identified as transporters that play in detoxification processes, later ABC transporters were shown to be required in stress responses, pathogen interaction [30]. In the present study, we compared the expression of two ABC transporter genes, ABCC5 and ABCC11, during bacterial wilt among ginger species. ABCC5 encodes a high-affinity inositol Hexa kis phosphate transporter, which plays a part in guard cell signalling and phytate storage [31]. This protein was reported to play a role in the defence mechanism in several plants [31, 32]. ABCC11 has reported being involved in developmental plasticity and stress responses [33]. ABCC5 and ABCC11 were differentially expressed among resistant and susceptible gingers. However, their expression was more evident in rhizome than leaves in both ginger species. Since bacterial wilt is a rhizome borne disease in ginger, these genes may play a critical role in disease resistance (Fig. 7B & H). Similarly, a difference in expression of ABC transporter genes was shown in Solanum sps when Globodera pallida infected resistant and susceptible plant roots [34].

PRT6 is a ubiquitin-protein ligase that is a component of the N-end rule pathway. It regulates the biosynthesis of plant-defence metabolites such as glucosinolates, and phytohormone jasmonate which play a crucial role in plant immunity [35]. In our experiment, PRT6 had higher expression in mango ginger leaves and rhizome tissues when compared to ginger (Fig. 7C). PRT6 mutants in Arabidopsis also showed higher susceptibility than wild type when infected with R. solanacearum [35].

NBS-LRR genes are one of the most studied disease resistance gene family in plants [36]. Several studies have been carried out in these genes in giving resistance in wheat, cassava, sugarcane, rice, apple, coconut [37, 38]. They are involved in detecting various biotic stresses such as pathogens, including bacteria, viruses, fungi, nematodes, insects, and oomycetes. Their action can lead to plant cell death in the typical hypersensitive response [39]. We compared the expression of an NBS-LRR protein, RPS2, which was also well characterized in other plants [40, 41]. There was a significant difference in the expression of RPS2 in mango ginger when compared to ginger (Fig. 7D). Moreover, the expression was higher in rhizome when compared to leaves. It was reported earlier that overexpression of RPS2 activates a downstream defence response pathway in Arabidopsis [42].

FRS6 (FAR1-related sequence 6) is a protein essential for phytochrome A controlled far-red responses in Arabidopsis. Loss-of-function mutants display elongated hypocotyls, specifically under continuous FR light (FRc) [43]. It was reported that during heat stress, this gene was downregulated in conic seagrass, Posidonia oceanica [44]. In agreement with previous reports, the FRS6 was downregulated in resistant ginger and upregulated in susceptible ginger (Fig. 7E).

AAP4 is an amino acid permease which is an amino acid-proton symporter. It is a stereospecific transporter and has broad specificity for neutral amino acids, such as alanine, asparagine, and glutamine. It was reported that amino acid transporters play a crucial role in plant defence by controlling amino acid transport [45, 46]. Significantly differential expression of AAP4 among tissues and ginger species was observed in our experiment (Fig. 7F). Thus, amino acid transporter might have a significant role in defence response against R. solanacearum.

WRKY19 is a transcription factor that acts as a disease resistance protein with a serine/threonine-protein kinase activity [47]. The TIR-NB-LRR pair DSC1 and WRKY19 contributed to basal immunity in Arabidopsis against the root-knot nematode Meloidogyne incognita [48]. Also, overexpression of wheat WRKY gene TaWRKY19 increased the salt, drought, and freezing tolerance in transgenic plants [49]. Our study showed high expression in ginger leaves when compared to mango ginger (Fig. 7G). Nevertheless, in the case of the rhizome, higher expression of WRKY 19 was observed in mango ginger. Thus, WRKY19 might have some critical role in defence response.

DNAJ1 are heat shock proteins (HSPs) that are molecular chaperones known for controlling inappropriate aggregation, folding, misfolding, and unfolding several proteins. They are reported to be involved in hyperosmotic and heat shock by preventing the aggregation of stress-denatured proteins and disaggregating proteins [50]. In this study, DNAJ1 was differentially expressed among different tissues of ginger species (Fig. 7I). These results proved its role in defence is tissue-specific and different in ginger species.

Comparison and validation of expression of selected miRNAs

Several reports of plant miRNA involvement in regulating signalling pathways, NBS-LRR gene expression, and ROS pathways during pathogen infestation are available. Here we studied the role of nine miRNAs during bacterial wilt in susceptible and resistant ginger species. As mentioned earlier, even though each miRTGs had targets for several miRNAs, data of miRNAs showing Pearson correlation coefficient with its corresponding miRNA target of more than 0.9 were only analyzed.

miR1886 was found to play an important role in abiotic and biotic stress in various plants. miR1886 was less expressed in response to ABA treatment related to an increase in Cf4CL transcripts [51]. In this study, ath-miR1886 targeted 4CLL1 in ginger and mango ginger. Ath-miR1886 was upregulated in mango ginger rhizome and downregulated in ginger leaves and rhizomes. This suggests that it is tissue-specific and species-specific. However, the expression difference among ginger species was a strong indication of miR1886’s role in bacterial wilt resistance (Fig. 7A).

Several extended studies were carried out to understand the role of miRNA mediated regulation of ABC transporters. Here we studied mtr-miR5261 and gma-miR4415, which targets ABCC5 and ABCG11, respectively. Both miRNAs were previously reported to play defence response during biotic and abiotic stress [52,53,54]. Even though both miRNAs were expressed differentially among ginger species, gma-miR4415 seems to play effectively in defence against bacterial wilt since its expression was 10 to 20 times higher in rhizome than leaves of resistant one (Fig. 7B&H).

Osa-miR169, which targets PRT6, is a conserved miRNA family reported in plant growth, development and responses induced by stress. In rice, osa-miR169 negatively regulate the immune response against blast fungus, Magnaporthe oryzae. It proved that regulation was made by repressing the expression of nuclear transcription factor subunit alpha (NF-YA) genes. Moreover, osa-miR169 overexpressed transgenic rice lines were hyper susceptible to M. oryzae infection [55]. Our study clearly showed that osa-miR169 was down-regulated in both ginger and mango ginger, but less expression in ginger than mango ginger (Fig. 7C). Also, downregulation of this miRNA in the early stages proves its crucial role during R. solanacerum infection.

Gra-miR482 is an ancient and extensive family of miRNA present in all land plants. In cotton, it regulated the target site by cleaving and inhibiting the expression of target nucleotide-binding sites and leucine-rich repeat (NBS-LRR) [56]. In this study, the gra-miR482 mainly targets RPS2. Gra-miR482 was down-regulated in leaves and rhizomes of ginger but upregulated in mango ginger, even in the initial hours of the infection (Fig. 7D). These results imply that miR482 regulates the defence response of the NBS-LRR gene in ginger species.

Ppt-miR1223 targets mainly genes related to nucleic acid binding and secondary metabolic process. In ginger it targets mainly FRS6. miR1223 was reported previously in Allium cepa [57] and Physcomitrella patens [58]. This miRNA upregulated in the rhizome of both ginger and mango ginger, till 12 h post-infection (Fig. 7E). However, later this miRNA was down regulated in ginger rhizomes, indicating its role during the early phase of infection. Several miRNAs showed a differential expression in the early and late phase of the disease [59].

Mtr-miR164 was conserved, highly expressed in fruits, and validated to target a subset of the NAC-domain transcription factor gene family such as AAP4 [60, 61]. Aly-miR398 was a conserved miRNA identified in Arabidopsis which targets WRKY19 [62]. Both were expressed tissue specifically and more in the rhizomes of resistant ginger compared to susceptible one (Fig. 7F &G). However, the role played by these miRNAs on disease resistance or susceptibility is not clearly understood.

The ath-miR167 was reported to be important in correcting gene expression patterns and fertility in both ovules and anthers in Arabidopsis [63]. In this study it cleaved DNAJ1. Among the ginger species, the higher expression in mango ginger revealed its vital role in resistance (Fig. 7I).

Conclusions

Our study utilized the already available RNA-Seq data to identify and compare DEmiRTGs and their miRNAs in ginger and mango ginger against R. solanacearum. This strategy aided in developing a new miRNA seq data of 4736 and 4485 DEmiRTGs in ginger and mango ginger, respectively. Functional annotation results showed that several defence response terms were enriched specifically in resistant ginger. KEGG analysis revealed the crucial role of carbohydrate metabolism during bacterial wilt infection in ginger. Additionally, we identified nine miRNA/miRTGs key candidate pairs in response to R. solanacearum infection in ginger. Our results paved the way in understanding significant miRNA-mediated posttranscriptional regulation of bacterial wilt resistance in mango ginger. The data set generated is further helpful in exploring miRNAs function in improving ginger resistance to bacterial wilt.

Methods

miRNA target and miRNA prediction

To evaluate miRNAs’ role in response to bacterial wilt infection in ginger (susceptible) and mango ginger (resistant), we have used already available RNA-Seq data of ginger (PRJNA311170), and mango ginger (PRJNA315599) challenged with Ralstonia. The leaf tissues of three biological replicates were sampled over a 72 h period post-inoculation and pooled before RNA preparation. The two RNA libraries were constructed and sequenced using Illumina RNA-Seq method [64]. After sequencing, high-quality reads were retained after removing the adapter, low-quality sequences and PCR duplicates from the raw data using FASTQ Quality Check [65].

Those clean reads were utilized here to reconstruct the transcriptome de novo. Trinity-v2.11.0 software with the parameters: minimum contig length of 100 bases and average fragment length 300 bp was used [66]. These two predicted transcriptome data were utilized to predict conserved miRTGs and identify the miRNAs for each target. Both were carried out using the psRNATarget server [67]. In this research, the following default parameters were used for identifying potential miRTGs: (1) maximum expectation 3; (2) length for complementarity scoring (hsp size) 20; (3) target accessibility—allowed maximum energy to unpair the target site (UPE) 25; (4) flanking length around target site for target accessibility analysis 17 bp upstream and 13 bp downstream and (5) range of central mismatch leading to translational inhibition 9–11 nucleotides. For the prediction of miRNAs for each miRTGs, above mentioned psRNATarget server was used with default parameters.

Differential expression and functional analysis

Differential expression was conducted using edgeR [68] with significant miRTGs based on FDR-corrected p-value <  = 0.05. The number of reads mapped and Reads Per Kilobase of the transcript, per Million mapped reads (RPKM) were used to calculate fold change (FC). Transcripts with FC >  = 0.8 and P value <  = 0.05 was considered as upregulated and FC <  = -0.8 and P value <  = 0.05 were considered as downregulated. Blast2GO software with default settings was used in Gene Ontology (GO) annotation and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis to identify the putative biological functions and pathways of the DEmiRTGs. Initially, BLASTX aligned (E ≤ 1e-6) contigs to the NCBI non-redundant protein database and later GO terms were retrieved for each BLAST hit with default parameters. KEGG maps were also retrieved for each query sequence as per instructions [69].

Construction of regulatory network using miRNA-mRNA

Visualization of the regulatory network between miRNA and mRNA was constructed using Cytoscape 3.7.1 [70].

Plant material

For experimental validation, ginger (susceptible) and mango ginger (resistant) varieties used were IISR Varada and Amba, respectively. Disease-free rhizomes were obtained from the Experimental Farm of ICAR-Indian Institute of Spices Research, Peruvannamuzhi, Kerala, India. Sterile distilled water was used to wash the rhizome thoroughly. They were then planted in autoclaved perlite. The plants were maintained in the net house of the ICAR-Indian Institute of Spices Research, Kozhikode, Kerala.

Inoculum preparation and inoculation procedure

Initially, fresh cultures R. solanacearum strain GRs Mnt2 (virulent) grown using Casamino acid-Peptone-Glucose (CPG) medium were inoculated in CPG broth kept at 28 °C for 16 h with continuous shaking. Forty-five-day old, same sized plants were carefully removed from perlite and washed. Plants were incubated for five days in a beaker containing 150 ml virulent broth under greenhouse conditions. Similarly, fresh plants were maintained in sterile water as control. Plant tissues (leaves and rhizome) were collected form different time intervals post-inoculation (1, 4, 8, 12, 24, 48, 72, 96, and 120-h post-inoculation (hpi)).

Expression validation of using qPCR

Here quantitative RT-PCR (qRT-PCR) was performed to validate the expression of miRTGs and miRNAs on the Rotor-Gene Q real-time PCR system (Qiagen, USA). For each sample, there were three biological replicates. Stem-loop qRT-PCR [71] for miRNA and regular qPCR [72] for target genes were conducted as described previously. The fold change of the transcripts was calculated relative to the control (0 hpi) using the 2− ddCt method using β-actin as an internal control. List of primers used are available in Tables 2 and 3.

Statistical analysis

To evaluate the statistical validity of results, all experiments were performed three times, each having five replicates. IBM SPSS Statistics 24 was used for statistical analysis. Data shown are the mean value ± standard deviation of three independent experiments. One way ANOVA along with Tukey posthoc test at P < 0.05 level of significance was carried out. Correlation analysis between selected miRNAs and their targets were carried out using the Pearson coefficient.