Identification, genomic localization, and functional validation of salt-stress-related lncRNAs in Indian Mustard (Brassica juncea L.)

doi:10.21203/rs.3.rs-4704413/v1

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Identification, genomic localization, and functional validation of salt-stress-related lncRNAs in Indian Mustard (Brassica juncea L.)

https://doi.org/10.21203/rs.3.rs-4704413/v1

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Indian Mustard (Brassica juncea L.) is a globally cultivated winter oilseed crop of the rapeseed-mustard group. In India, it is mainly grown in the semi-arid northwest agroclimatic zone, known for high soil salinity. In this regard, enhancing salt stress tolerance in B. juncea is critical for sustaining its production in the region. Long non-coding RNAs (lncRNAs) play critical roles in coordinating gene expression under various abiotic stresses, including salt stress, but their involvement in salt stress response in B. juncea remains largely unknown. In this study, we conducted RNA-seq analysis on control, salt-stressed, and salt-shocked young leaves of the salt-tolerant B. juncea var. CS-52. We identified 3,602 differentially expressed transcripts between stress versus control and shock versus control samples. Among these, 61 were identified as potential lncRNAs, with 21 specific to salt stress and 40 specific to salt shock. Out of the 21 lncRNAs specific to salt stress, 15 were upregulated and six were downregulated, while all 40 lncRNAs unique to salt shock were downregulated. The chromosomal distribution analysis of lncRNAs revealed their uneven placement across 18 chromosomes in B. juncea. RNA-RNA interaction analysis identified 26 interactions between 10 lncRNAs and 23 miRNAs and predicted 13 interactions between six miRNAs and 13 mRNAs. Finally, six lncRNA-miRNA-mRNA interaction networks were established, involving five lncRNAs, 13 miRNAs, and 23 mRNAs. qRT-PCR analysis revealed the upregulation of four out of five lncRNAs along with their target mRNAs, supporting their involvement in the salt stress response in B. juncea.

Brassica juncea

lncRNA

eTMs

salt stress

salt shock

Salinity poses a global environmental constraint, leading to reduced crop productivity and quality [1]. It induces extensive physiological dysfunctions in plants, with the severity depending on the genotype, plant growth phase, and ionic strength of the salt source [2]. Gradual exposure to increasing salt concentrations or sustained exposure to low salinity levels results in salt stress, while instant imposition of high salinity levels causes salt shock. Salt stress is a recurring challenge for crops in arid and semi-arid areas, whereas salt shock occurs rarely, typically in events such as tsunamis leading to seawater entering cultivated land [3–5].

Indian Mustard (Brassica juncea L.) (AABB; 2n = 36) is a globally cultivated winter oilseed crop. It dominates the rapeseed-mustard group in India, covering over 90% of the total acreage [6]. Second only to groundnut in the oilseed economy, it contributes 20% to total oilseed production. The distribution of B. juncea is concentrated in the semi-arid northwest agroclimatic zone of India, characterized by elevated salinity levels in groundwater and moderate to high sodicity issues [7]. Understanding and improving its tolerance to salt stress are crucial to boost and sustain B. juncea production in India.

In higher organisms, less than 3% of genetic transcripts encode proteins. The remaining transcriptome consists of non-coding RNA (ncRNA) transcripts, previously considered as transcriptional noise. However, recent high-throughput sequencing and experimental validation have substantiated the participation of ncRNAs in various gene regulation processes, including epigenetic, transcriptional, and post-transcriptional [8]. Classified as housekeeping or regulatory based on origin, biogenesis, and mode of action, ncRNAs play crucial roles in plant growth, development, and abiotic stress responses. Among regulatory ncRNAs, long non-coding RNAs (lncRNAs) (transcripts longer than 200 nucleotides) account for a significant share and can impact diverse biological and metabolic processes, aiding plants in surviving abiotic and biotic challenges [9].

A number of lncRNAs are reported in several plant species with their regulatory mechanisms partially revealed in stress response, phosphate homeostasis, and male sterility [10, 11]. Salt stress-related lncRNAs have been identified in various plants, such as Arabidopsis, Gossypium hirsutum, Medicago truncatula, Triticum aestivum, Glycine max, Pistacia vera, and Sorghum bicolor [12–18]. Notably, lncRNAs like DRIR in Arabidopsis and Mulnc1 in mulberry are reported to be associated with salt stress response [19, 20]. While research in this area has advanced for several plant species, there is a significant gap in lncRNA research under elevated salt stress environments in B. juncea.

This study analyzed RNA-seq data from control, salt-stressed, and salt-shocked leaf samples of B. juncea to identify differentially expressed lncRNAs under salt stress and shock conditions. The study identified target genes regulated by lncRNAs or miRNA-mediated mechanisms. The constructed lncRNA-miRNA-mRNA networks involving functionally annotated targets revealed the involvement of lncRNAs in the regulation of multiple processes. Additionally, the study predicted five salt-stress-related lncRNAs as endogenous target mimics (eTMs) for six miRNAs and quantitatively compared the expression of the lncRNAs and their mRNA targets under salt stress condition. Differential expression patterns of lncRNAs and target mRNAs under salt-stress versus control conditions suggested that lncRNAs may be involved in salt stress responses in B. juncea. The findings of this study are anticipated to contribute to a deeper understanding of the complex regulatory framework involving lncRNAs in salt tolerance, laying the foundation for targeted interventions and functional validations to enhance salt stress tolerance in B. juncea.

2.1. RNA-seq data acquisition and reference-based transcriptome assembly

We obtained raw RNA-seq reads from the NCBI Sequence Read Archive (SRA) database, generated from control and salt-stressed [21] as well as salt-shocked [22] young leaves of B. juncea var. CS-52. The quality of the raw reads was assessed using FastQC v0.11.9 software [23]. Trimmomatic v0.39 software [24] was used to remove adaptor contamination and low-quality reads, with an average length (nucleotides) and quality score cut-off set at 36 and 25, respectively. HISAT2 v2.1.1 software [25] was used with default parameters to align the clean reads to the B. juncea reference genome (Braju_tum_V2.0) downloaded from the BRAD v3.0 database (http://brassicadb.org) [26]. The genome was indexed using the hisat2-build command available in the HISAT2 v2.1.1 software. The output SAM file from HISAT2 was sorted using SAMtools v1.16 software [27]. Cufflinks v2.2.1 software [28] was used to assemble aligned reads into full-length transcripts, quantify expression levels for each transcript, and calculate expression differences among control, salt-stressed, and salt-shocked leaf samples. Transcripts with FPKM values > 0.5 and lengths > 200 nucleotides were considered for further downstream analysis [29].

2.2. Identification of differentially expressed transcripts

Differentially expressed transcripts between salt stress versus control and salt shock versus control samples were analysed using edgeR [30]. Differentially expressed transcripts with P values ≤ 0.05 and log2 fold-changes ( ≥ + 2/ ≤ -2) were considered significantly differentially expressed. The Venny v2.1.0 software [31] was used to identify common and unique differentially expressed transcripts between stress versus control and shock versus control samples.

2.3. Functional annotation of differentially expressed transcripts

The functional annotation of differentially expressed transcripts was conducted using the Blast2GO pipeline within OmicsBox v2.1.14 software [32]. To identify homologs, transcript query sequences were compared against the NCBI Viridiplantae protein sequence database using blastx-fast, with an e-value threshold of 1.0E-3, a percent identity of ≥ 95%, and a hit number threshold of 20. Subsequently, the blast results were used for gene ontology (GO) mapping [33] and annotation. To improve annotation quality, the integration of GO terms from InterProScan was combined with GO annotations obtained from GO mapping, ensuring the inclusion of reliable plant-specific GO terms. To unveil plant-related metabolic and biological pathways associated with the differentially expressed transcripts, OmicsBox v2.2.14 software was used to determine KEGG pathways [34] using the KEGG pathway tool with default parameters.

2.4. In silico prediction and genomic localization of lncRNAs

All the differentially expressed transcripts were subjected to a blast search against the NCBInr database. Transcript sequences lacking blast hit in the NCBInr database were then analyzed using the PLEK v1.2 [35] and the CPC2 [36] software to identify non-coding RNAs (ncRNAs). Venny v2.1.0 software was used to identify common and unique ncRNAs predicted by both software. The common ncRNAs predicted by PLEK and CPC2 were further subjected to an hmmscan search against the Pfam database to eliminate transcripts with probable coding potential. Transcripts lacking Pfam hits were then searched against the Rfam database to filter out small ncRNAs (miRNA, snRNA, tRNA, rRNA, and snoRNA). The remaining ncRNAs were considered potential lncRNAs and were annotated using the RNAcentral database. The genomic location of each lncRNA was determined through a BLASTn search against the B. juncea reference genome. The MapChart v2.32 software was used to plot the chromosome-wise position of the lncRNAs [37].

2.5. Analysis of lncRNA-miRNA interaction and prediction of eTMs

We retrieved 547 Brassicaceae-specific miRNAs (Arabidopsis thaliana − 11; Brassica campestris − 8; Brassica napus − 237; Brassica oleracea − 11; Brassica rapa − 168; and Camelina sativa − 112) from miRBase (https://www.mirbase.org/) (Supplementary Table S1). The interaction between salinity-related lncRNAs and Brassicaceae-specific miRNAs was predicted using the plant small RNA target (psRNATarget) analysis server [38]. psRNATarget was executed with the default parameters: expectation = 5, penalty for the G:U pair = 0.5, penalty for other mismatches = 1, extra weight in seed region = 1.5, seed region = 2–13 nucleotides, HSP size = 19, penalty for opening gap = 2, penalty for extending gap = 0.5, translation inhibition range = 10–11 nucleotides, and bulges (gap) -allowed.

2.6. Prediction of eTM targets

All the miRNAs showing interaction with the salinity-related lncRNAs (eTMs) were subjected to the identification of their mRNA targets using the psRNATarget analysis server. The psRNATarget analysis was conducted with standard parameters, including selecting the top 200 targets, setting the expectation value ≤ 3.0, assigning a penalty of 0.5 for G:U pairs, a penalty of 1 for other mismatches, an extra weight of 1.5 in the seed region, specifying the seed region from the 2nd to the 13th nucleotide, and establishing a translation inhibition range spanning the 10th and 11th nucleotides [38].

2.7. Imposition of salt treatment and sample collection

Seeds of B. juncea var. CS-52 were surface sterilized in a 2.5% sodium hypochlorite solution containing 0.1% Tween-20 for 30 minutes, followed by three washes for 10 minutes each with autoclaved double-distilled water [21]. The in vivo germination of sterilized seeds involved placing them on two layers of autoclaved water-moistened Whatman No.1 filter paper in 9 cm Petri dishes. Subsequently, the dishes were incubated at 25°C in the dark for 72 hours. Following germination, the seedlings were transferred to a continuous air-bubbling hydroponic floating raft system (18 trays, each with 15 seedlings) within a Conviron plant growth chamber maintained at 22 ± 2°C, with a 16-hour light/8-hour dark photoperiod at a light intensity (photon flux density) of 100 µmole m − 2 s − 1. The seedlings were nurtured using the standard Hoagland solution [39] for 15 days. Subsequently, the 18 trays were divided into three groups. The first group, which served as controls, continued to receive the standard Hoagland solution. The second and third groups were subjected to salinity stress and shock, respectively. For the second group, salinity stress was imposed by gradually increasing NaCl concentration in the Hoagland solution as follows: 50 mM for two days, 75 mM for two days, 100 mM for two days, and 125 mM for two days. After eight days of exposure to gradually increasing salinity, the seedlings in this group were subjected to an application of 150 mM NaCl. Simultaneously, seedlings in the third group, maintained with the standard Hoagland solution, were subjected to a single application of 150 mM NaCl, inducing salt shock. Throughout the experiment, we replaced the Hoagland solution every 24 hours. Leaf samples were collected from the salt-stressed and shocked plants 3 hours after the 150 mM NaCl treatment. In parallel, leaf samples were also collected from the control group. The harvested samples were cleaned with autoclaved double-distilled water and immediately frozen in liquid nitrogen.

2.8. qRT-PCR-based quantitative expression analysis of salt stress- and shock-related lncRNAs and mRNA targets

To compare the expression patterns of salt stress and shock-related lncRNAs with their target mRNAs in B. juncea, quantitative RT-PCR (qRT-PCR) was used. Forward and reverse primers were designed using the IDT oligo analyzer tool (https://www.idtdna.com/calc/Analyzer), following the MIQE guidelines [40] (Supplementary Table S2). Total RNA from three biological replicates of the control, salt-stressed, and shocked leaf samples at 3 hours post-salt stress and shock, respectively, was isolated using the RNeasy Plant Mini Kit (Qiagen, Germany). The amount, integrity, and quality of the isolated RNA were confirmed using a Nanodrop spectrophotometer and electrophoresis on a 1.2% agarose gel, respectively. Total RNA was converted to cDNA using the SuperScript™ III First-Strand Synthesis Kit (Invitrogen, USA). qRT-PCR was performed in optical 96-well plates (Applied Biosystems) on a StepOnePlus qRT-PCR System (Applied Biosystems, USA) using the SYBR Master Mix (Applied Biosystems, USA). The PCR program included an initial denaturation step at 95°C for 10 min, followed by 40 cycles at 95°C for 15 s and 60°C for 60 s, and a melt cycle from 60 to 95°C in 0.5-s increments. The efficiencies of the qRT-PCR primers were calculated using cDNA template dilution series, and amplification specificity was verified using melting-curve analysis. The B. juncea ubiquitin gene served as the internal reference gene for both lncRNA and target mRNA expression studies [41]. Relative expression levels between different samples were calculated using the 2–ΔΔCT method [42].

3.1. Transcriptome data processing and reference-based assembly

A total of 388,580,960 raw reads were retrieved from the SRA database. Cleaning the raw reads for adaptors and low-quality reads resulted in 347,040,952 clean reads. Among these, 320,268,780 (92.30%) reads were successfully aligned to the B. juncea reference genome using HISAT2 v2.1.1 software (Supplementary Table S3). Using Cufflinks v2.2.1 software, reference-based assembly of clean reads yielded 100,829 transcripts. For further analysis, 50,717 transcripts > 200 nucleotides with FPKM values > 0.5 were used. Figure 1 depicts the pipeline used for analysing the data. Differentially expressed transcripts between salt stress versus control and salt shock versus control samples were analyzed using edgeR with P value cut-offs < 0.05 and log2 fold-change ≥ 2 or ≤ -2. A total of 3,602 differentially expressed transcripts were identified between salt stress versus control and salt shock versus control samples. Among these, 1,621 (45%) and 1,827 (50.7%) transcripts were unique to stress versus control and shock versus control samples, respectively, while 154 (4.3%) transcripts were common between them (Fig. 2).

3.2. Identification of lncRNAs

We annotated the differentially expressed transcripts by performing blastx searches against the NCBInr database. A total of 89.5% (3,224) differentially expressed transcripts exhibited a blast hit against the NCBInr database, while the remaining 10.5% (378) showed no substantial similarity to known proteins. Transcripts without a blast hit in the NCBInr database were used to identify ncRNAs using PLEK and CPC2 software. The transcript analysis, using both software, commonly identified 73 non-coding transcripts. An hmmscan search against the Pfam database was used to further eliminate 12 transcripts with coding potential. The blastn searches against the Rfam database using the remaining 61 ncRNAs as queries did not reveal any significant hits or similarity to other ncRNAs (rRNA, miRNA, siRNA, snRNA, tRNA, snoRNA, etc.). Consequently, we considered all 61 ncRNAs as potential lncRNAs (Supplementary Table S4). Out of the 61 differentially expressed potential lncRNAs, 21 were specific to salt stress, while 40 were specific to salt shock. Fifteen of the 21 salt-stress-specific lncRNAs were upregulated, while six were downregulated. All 40 salt-shock-specific lncRNAs showed a downregulation.

3.3. In silico characterization of lncRNAs

The analysis of the chromosomal location of the 61 salinity-related lncRNAs revealed their uneven distribution across the 18 chromosomes of B. juncea (A01–A10, B01–B08) (Fig. 3; Supplementary Table S5). Chromosome A09 contained the highest number (07), while chromosomes A04 and A05 contained the lowest number (01) of lncRNAs. The lncRNAs varied in length from 201 to 435 bp, with an average of 268.38 bp. A total of 32 (52.5%) of the lncRNAs had sizes ranging between 200 and 250 bp.

3.4. Annotation of salinity-related differentially expressed transcripts and lncRNAs

Based on GO annotation using the Blast2GO pipeline, GO terms were assigned to 638 (19.8%) out of 3,224 differentially expressed protein coding transcripts, and they were classified into three main categories: biological process (BP) with 205 (32.1%), molecular function (MF) with 309 (48.4%), and cellular components (CC) with 124 (19.4%) transcripts. In the BP category, phosphorylation (5.4%), translation (4.9%), and DNA replication (2.4%) accounted for the majority of the transcripts (Fig. 4a), while in the MF category, metal ion binding (6.5%), ATP binding (4.9%), and DNA binding (3.9%) (Fig. 4b), and in the CC gategory membrane (20.2%), cytoplasm (17.7%), plasma membrane (8.1%), and nucleolus (7.2%) accounted for the maximum number of transcripts (Fig. 4c). Using the RNAcentral database, all the 61 salinity-related potential lncRNAs were annotated as "lncRNA" (Supplementary Table S4). A total of 25 lncRNAs were identified in Brassica spp., and the rest were found in various plant species such as Arabidopsis thaliana (6), Actinidia chinensis (5), Malus domestica (4), Capsella spp. (3), Populus spp. (2), Panax ginseng (2), Musa acuminata (2), Gossypium spp. (2), Fragaria vesca (2), Coffea arabica (2), Vitis vinifera (1), Setaria italica (1), Lactuca sativa (1), Eucalyptus grandis (1), Carica papaya (1), and Ananas comosus (1).

3.5. Analysis of lncRNA-miRNA interactions and prediction of eTMs and their mRNA targets

Using psRNATarget software, pairing analyses between 15 salt stress-upregulated lncRNAs and 547 Brassicaceae-specific miRNAs were conducted. As a result, a total of 26 interactions with expectation values ≥ 5 were identified between 10 lncRNAs and 23 miRNAs (Supplementary Table S6). Using psRNATarget software, all 23 miRNAs were selected for predicting mRNA targets, revealing a total of 13 interactions between six miRNAs and 13 mRNAs with expectation values ≤ 2.5 (Supplementary Table S7). Finally, a total of six eTMs, involving five lncRNAs, six miRNAs, and 13 target mRNAs, were predicted based on lncRNA-miRNA-mRNA interaction analysis (Table 1).

Table 1

Salt-stress-related eTMs identified in *B. juncea.*
LncRNA	miRNA	Target mRNA	Target gene annotation	Function of target gene	Reference
Bju_lncRNA006	bra-miR-396-5p	BjuVA01G16450	MADS-box protein AGL24	Salt tolerance	Castelán-Muñoz et al. 2019
Bju_lncRNA019	bna-novel-12-5p	BjuVB04G12640	LRR receptor-like serine/threonine-protein kinase	Salt tolerance	Alam et al. 2010
Bju_lncRNA025	ath-miR-854a	BjuVA06G46870	3-ketoacyl-CoA synthase 20	Germination/seeding-stage salt tolerance	Yang et al. 2020
		BjuVB04G12370	3-ketoacyl-CoA synthase 20	Germination/seeding-stage salt tolerance	Yang et al. 2020
		BjuVA08G30140	Purine permease 14-like	Cytokinin transport	Yin et al. 2020
Bju_lncRNA031	cas-miR-156i	BjuVB05G13850	Putative cysteine-rich receptor-like protein kinase 23	Disease resistance	Quezada et al. 2019
Bju_lncRNA031	cas-miR-159c-5	BjuVB03G12830	Chaperone protein dnaJ 11	Drought tolerance	Fan et al. 2017
Bju_lncRNA031	cas-miR-159c-5	BjuVA10G03050	7-hydroxymethyl chlorophyll a reductase	ROS tolerance	Piao et al. 2017
Bju_lncRNA043	cas-miR-858	BjuVB02G28640	MYB59	Salt tolerance	Sukumaran et al. 2023
		BjuVB02G56810	MYB111	Regulates flavonoid biosynthesis	Li et al. 2019
		BjuVB06G37680	MYB13	Salt/drought tolerance	Huang et al. 2018 Sukumaran et al. 2023
		BjuVA07G33590	MYB20	Salt tolerance	Cui et al. 2013
		BjuVA07G14640	MYB3	Salt tolerance	Kim et al. 2022 Sukumaran et al. 2023

3.6. Experimental validation

We used qRT-PCR to quantitatively assess the expression levels of five lncRNAs that were found to act as potential target mimics for six salt-stress-related miRNAs, as well as their corresponding target mRNAs. Under salt stress conditions, four lncRNAs, namely Bju_lncRNA006, Bju_lncRNA019, Bju_lncRNA025, and Bju_lncRNA043, acting as potential target mimics for four miRNAs, exhibited significant upregulation, with fold changes ranging from 5.2 (Bju_lncRNA019) to 9.4 (Bju_lncRNA006 and Bju_lncRNA025). Concurrently, the expression levels of the corresponding target mRNAs also exhibited significant upregulation (Fig. 5). The observed differences in expression levels for both lncRNAs and their cognate salt-stress-related mRNAs provide compelling evidence, suggesting that lncRNAs may be involved in salt stress responses in B. juncea.

Soil salinity is one of the most detrimental abiotic stressors affecting the yield and quality of the mustard crop worldwide [21]. Hence, enhancing salt tolerance in the mustard crop is crucial for its cultivation in salt-affected areas [43]. Salt tolerance is a complex trait governed by numerous genes regulated by an array of regulatory elements, including ncRNAs [44]. Among the ncRNAs, lncRNAs are emerging as key regulators of gene expression. Previous research has characterized many lncRNAs and proposed several mechanisms of their action. The most fundamental mechanism involves sequestering regulatory biomolecules, including miRNAs, which negatively regulate the expression of the target genes at the post-transcriptional level [45]. In this sense, the present research primarily aimed at the identification and functional validation of the lncRNA-miRNA-mRNA target networks affecting salt tolerance responses in B. juncea.

In this study, we used 388,580,960 raw RNA-seq reads obtained from the NCBI SRA database. These reads were derived from both control and salt-stressed, as well as salt-shocked, young leaves of B. juncea. Further, the reads were processed into 347,040,952 clean reads. Impressively, 92.30% of the clean reads effectively aligned to the reference genome of B. juncea, indicating that the RNA-seq data used in the present study was of sufficiently good quality [46]. The empirical analysis of the RNA-seq data identified 3,602 differentially expressed transcripts. Among these transcripts, 1,621 (45%) were specific to salt stress, 1,827 (50.7%) were specific to salt shock, and 154 (4.3%) were common to both. These results indicate that the patterns of gene expression vary in response to salt stress and salt shock and signify the method of salt application in genetic and molecular studies [5].

Under salt stress conditions, lncRNAs have been implicated in mediating both osmotic and ionic stress responses in plants [47]. Consistent with the broader role of lncRNAs as key regulators in plant stress responses [48], our study identified 61 lncRNAs differentially expressed under salt-stress/shock conditions in B. juncea. Notably, under shock conditions, 40 of these lncRNAs were downregulated, corroborating their role in repressing gene expression during sudden stress events [49]. Moreover, we found 21 lncRNAs expressed under stress conditions, of which six were downregulated and 15 were upregulated. This differential expression pattern highlights the intricate regulatory dynamics of lncRNAs in modulating responses to varying stress intensities, underscoring their importance in fine-tuning plant adaptive strategies [49].

MicroRNAs (miRNAs) are recognized as key players in bolstering plant resilience against abiotic stressors [50]. Under salt-stress conditions, the intricate interplay between miRNAs and lncRNAs orchestrates a regulatory network that profoundly influences the expression of salt-stress-responsive genes. Notably, lncRNAs actively participate in competitive interactions within the competing endogenous RNA (ceRNA) network [51]. Consistent with this fact, we unveiled 26 interactions involving 10 salt-stress-specific lncRNAs and 23 crucifer miRNAs. Finally, a total of six eTMs, involving five lncRNAs, six miRNAs, and 13 target mRNAs, were predicted based on lncRNA-miRNA-mRNA interaction analysis. Notably, all the miRNA targets were found to be directly involved in salt-stress responses in various crucifers. Most of the miRNA targets were found to encode either transcription factors or transporters. Remarkably, among the six eTMs encompassing five lncRNAs, six miRNAs, and 13 mRNAs identified, only four lncRNAs exhibited upregulation along with their mRNA targets. Similar co-expression analysis of lncRNAs with target mRNAs has been conducted in several other crops, and lncRNAs have been implicated to affect the expression of transcripts encoding stress-related transcription factor families, namely WRKY, NAC, MYB_related, ERF, C2H2, bZIP, and bHLH [18, 52]. These findings substantiate the emerging roles of the lncRNA-miRNA-mRNA regulatory network in governing the salt stress response in crop plants. Overall, the present research sets the groundwork for exploring molecular complexities in salt stress response in B. juncea, offering avenues for functional validation, targeted manipulation, and the potential development of salt-tolerant varieties.

The present study highlighted the regulatory significance of lncRNAs in orchestrating the molecular intricacies of the salt stress response in B. juncea. Using RNA-seq analysis, we identified 3,602 transcripts that were differentially expressed in response to salt stress and shock. Of these, 61 were identified as potential lncRNAs, with 21 being specific to salt stress and 40 to salt shock. This differential expression pattern emphasizes the nuanced variations in gene regulation under distinct stress conditions, highlighting the relevance of salt application methods in genetic and molecular investigations. Mapping lncRNAs onto the B. juncea reference genome revealed an uneven chromosomal distribution across 18 chromosomes, suggesting potential genomic regulatory hotspots. Moreover, RNA-RNA interaction analysis unveiled intricate networks comprising 10 lncRNAs, 23 miRNAs, and 13 mRNAs, elucidating the interconnected regulatory dynamics underlying salt stress adaptation. Notably, qRT-PCR validation corroborated the upregulation of a subset of lncRNAs under salt stress, implicating their central role in modulating adaptive responses in B. juncea.

lncRNAs - Long non-coding RNAs; miRNA – MicroRNA; RT-PCR - Real time PCR; eTM - endogenous Target Mimics; B. juncea - Brassica juncea; ncRNC - non-coding RNA; GO - Gene Ontology

Ethics approval and consent to participate

Not applicable

Consent for publication

The authors of this manuscript have no competing interests.

Funding

This study was funded by ICAR - Indian Institute of Agricultural Biotechnology, Ranchi 834 003, Jharkhand, India.

Authors' contributions

BKS conceived the project and wrote the manuscript. BKS, KUT, BS, and AP designed the experiments. MS, TM, KUT performed the experiments. KUT, TM, SK, and BS analyzed the data. AP coordinated the project. All the authors read and approved the manuscript.

Acknowledgements

We sincerely acknowledge the Director, ICAR - Indian Institute of Agricultural Biotechnology, Ranchi 834 003, Jharkhand, India, for providing financial support and the facilities to carry out this research work.

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Identification, genomic localization, and functional validation of salt-stress-related lncRNAs in Indian Mustard (Brassica juncea L.)

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Abstract

Figures

1. Introduction

2. Materials and methods

2.1. RNA-seq data acquisition and reference-based transcriptome assembly

2.2. Identification of differentially expressed transcripts

2.3. Functional annotation of differentially expressed transcripts

2.4. In silico prediction and genomic localization of lncRNAs

2.5. Analysis of lncRNA-miRNA interaction and prediction of eTMs

2.6. Prediction of eTM targets

2.7. Imposition of salt treatment and sample collection

2.8. qRT-PCR-based quantitative expression analysis of salt stress- and shock-related lncRNAs and mRNA targets

3. Results

3.1. Transcriptome data processing and reference-based assembly

3.2. Identification of lncRNAs

3.3. In silico characterization of lncRNAs

3.4. Annotation of salinity-related differentially expressed transcripts and lncRNAs

3.5. Analysis of lncRNA-miRNA interactions and prediction of eTMs and their mRNA targets

3.6. Experimental validation

5. Discussion

5. Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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Version 1