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Insights into lncRNA-mediated regulatory networks in Hevea brasiliensis under anthracnose stress

Plant Methods volume 20, Article number: 182 (2024) Cite this article

Abstract

In recent years, long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) have emerged as critical regulators in plant biology, governing complex gene regulatory networks. In the context of disease resistance in Hevea brasiliensis, the rubber tree, significant progress has been made in understanding its response to anthracnose disease, a serious threat posed by fungal pathogens impacting global rubber tree cultivation and latex quality. While advances have been achieved in unraveling the genetic and molecular foundations underlying anthracnose resistance, gaps persist in comprehending the regulatory roles of lncRNAs and miRNAs under such stress conditions. The specific contributions of these non-coding RNAs in orchestrating molecular responses against anthracnose in H. brasiliensis remain unclear, necessitating further exploration to uncover strategies that increase disease resistance. Here, we integrate lncRNA sequencing, miRNA sequencing, and degradome sequencing to decipher the regulatory landscape of lncRNAs and miRNAs in H. brasiliensis under anthracnose stress. We investigated the genomic and regulatory profiles of differentially expressed lncRNAs (DE-lncRNAs) and constructed a competitive endogenous RNA (ceRNA) regulatory network in response to pathogenic infection. Additionally, we elucidated the functional roles of HblncRNA29219 and its antisense hbr-miR482a, as well as the miR390-TAS3-ARF pathway, in enhancing anthracnose resistance. These findings provide valuable insights into plant-microbe interactions and hold promising implications for advancing agricultural crop protection strategies. This comprehensive analysis sheds light on non-coding RNA-mediated regulatory mechanisms in H. brasiliensis under pathogen stress, establishing a foundation for innovative approaches aimed at enhancing crop resilience and sustainability in agriculture.

Introduction

In recent years, long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) have emerged as pivotal regulators in plant biology, orchestrating complex gene regulatory networks [1, 2]. LncRNAs, exceeding 200 nucleotides in length, influence gene expression through chromatin remodeling, mRNA stability modulation, and competitive endogenous RNA (ceRNA) interactions, thereby impacting development, environmental responses, and stress adaptation [3, 4]. Concurrently, miRNAs, approximately 20–24 nucleotides long, regulate target gene expression post-transcriptionally by guiding the RNA-induced silencing complex (RISC)-mediated mRNA degradation or translational repression [5,6,7]. The synergistic roles integrate transcriptional and post-transcriptional regulation, crucial for plant growth, development, and stress resilience [8,9,10,11].

In the field of disease resistance in H. brasiliensis, the rubber tree, significant strides have been made in understanding its response to anthracnose disease. Anthracnose, caused by fungal pathogens, poses a substantial threat to rubber tree cultivation worldwide, impacting both the yield and quality of latex production [12, 13]. While efforts have been made to decipher the genetic and molecular bases of anthracnose resistance in H. brasiliensis, researchers have elucidated various aspects of the plant’s defense mechanisms, including the roles of protein-coding genes [14, 15], signaling pathways [16, 17], and phytohormones [18] involved in pathogen recognition and response. However, despite these advancements, our knowledge of the regulatory roles of lncRNAs and miRNAs in the context of anthracnose stress in H. brasiliensis remains limited. Given the fact that these years, accumulating evidences in animals and in human beings have shown that lncRNAs, together with miRNAs, play critical roles in gene expression regulation, disease progression, and immune responses during infection [19,20,21], and similar regulatory roles for lncRNAs and miRNAs have been identified in various plant species in response to biotic and abiotic stresses [22,23,24], it is reasonable to hypothesize that similar mechanisms might also exist in the rubber tree. These mechanisms could be mediated by lncRNAs and miRNAs, potentially playing a crucial role in the rubber tree’s response to pathogen infection.

Accordingly, in this study, we integrated lncRNA sequencing, miRNA sequencing, and degradome sequencing to explore the regulatory landscape of lncRNAs and miRNAs in H. brasiliensis under anthracnose stress. Using RNA sequencing (RNA-Seq) and weighted gene co-expression network analysis (WGCNA), we detected differentially expressed lncRNAs (DE-lncRNAs) distributed across chromosomes and, with notable enrichment near telomeric regions. Our investigation revealed a sophisticated ceRNA regulatory network, which is pivotal in modulating pathways essential for plant defense mechanisms. Furthermore, we elucidated the functional role of HblncRNA29219 and antisense-located hbr-miR482a and the miR390-TAS3-ARF pathway in enhancing anthracnose resistance, offering profound insights into plant-microbe interactions and promising implications for agricultural crop protection strategies.

Materials and methods

Plant materials and treatment

The experiment utilized inoculated samples collected at four consecutive stages (initial inoculation, 24 h, 48 h, and 72 h post-inoculation) from immature leaves of rubber tree clones Reyan7–33–97, grown at the experimental plantation of Hainan University (Yazhou, Hainan, China). Prior to inoculation, C. gloeosporioides mycelium was cultured in PD liquid media for 2 days, and the conidia were harvested by centrifugation at 4000 rpm. The spore solution was adjusted to a concentration of 5 × 105 CFU/mL using sterile water and evenly sprayed onto the rubber tree leaves. Inoculated samples were incubated at 25 °C with humidity maintained at no less than 90%. At predetermined time points, samples were collected and immediately submerged in liquid nitrogen for subsequent experimentation. Each treatment was replicated three times biologically.

RNA extraction, library preparation, and sequencing

Total RNA was extracted from leaves using TRIzol reagent (Thermo Fisher Scientific, 15596018). RNA quality and purity were evaluated using the Bioanalyzer 2100 and RNA 6000 Nano LabChip Kit (Agilent, CA, USA). Ribosomal RNA (rRNA) was removed from total RNA using the Ribo-Zero Gold rRNA Removal Kit (Illumina, San Diego, USA). For lncRNA and mRNA sequencing, strand-specific libraries (fr-first strand) were constructed following the manufacturer’s protocol and sequenced on an Illumina NovaSeq™ 6000 platform (LC Bio, China) with paired-end reads of 2*150 bp (PE150). Small RNA (sRNA) sequencing libraries were prepared using the TruSeq Small RNA Sample Prep Kit (Illumina, San Diego, USA) and sequenced on an Illumina HiSeq 2000 instrument (LC Bio, Hangzhou, China) with single-end reads of 50 bp (SE50). Thirty qualified libraries were generated from Reyan7–33–97 inoculated with C. gloeosporioides for RNA and sRNA sequencing, respectively. For degradome sequencing, three samples from the initial inoculation stage were pooled to construct one degradation library following the vendor’s recommended protocol, and sequencing was performed using Illumina HiSeq 2000 (LC Bio, Hangzhou, China) with single-end reads of 50 bp. All raw sequencing data generated during this experiment can be accessed via the NCBI SRA database (BioProject ID: PRJNA1144109).

Identification of lncRNAs, miRNAs and miRNA target genes

The raw RNA-Seq reads from all samples underwent quality control using FastQC v0.12.1 [25], followed by preprocessing steps that included the removal of adapters, low-quality ends, and reads containing poly-N bases using Cutadapt v4.6 [26]. The resulting clean reads were then mapped to the rubber tree genome ASM3005281v1 using HISAT2 v2.2.1 [27]. For mRNA analysis, StringTie v2.1.7 [28] was employed to assemble transcripts and merge them across different samples iteratively. Expression levels were quantified using fragments per kilobase of transcript per million mapped reads (FPKM) with StringTie.

To identify lncRNAs, we initially utilized GffCompare v0.11.2 [29] to compare the assembled transcripts with the rubber tree genome annotation, retaining those transcripts with class codes “i”, “u”, “o”, “x”, “j”, and a length exceeding 200 bp. Subsequently, CNCI v2.0 [30], CPC2 v1.0.1-0 [31], pfam_scan v1.6-4 [32], and Transeq (EMBOSS v6.6.0.0) [33] were employed to predict transcripts with coding potential. Transcripts with coding potential were then filtered out, leaving behind those deemed as lncRNAs. DE-lncRNAs were identified using edgeR [34] with criteria of |log2 (fold change) | ≥ 1 and a p-value < 0.05, with genes meeting the criteria of p-value < 0.05 and |log2 (fold change) | ≥ 1 considered as differentially expressed mRNAs (DE-mRNAs). The chromosomal locations of DE-lncRNAs were visualized using TBtools v2.065 [35].

For miRNA analysis, adaptors (5’ adapter: 5’-GTTCAGAGTTCTACAGTCCGACGATC-3’, 3’ adapter: 5’-TGGAATTCTCGGGTGCCAAGG-3’) and low-quality reads were trimmed from the raw data generated in sRNA sequencing using Cutadapt v4.6 as well. Known miRNAs were identified by matching sequences to those in the miRBase database [36] with fewer than three mismatches, and their precursors, capable of folding into stem-loop structures, were validated. Novel miRNAs were predicted by aligning unmapped sequences against the rubber tree genomes, with the RNAfold WedServer [37] (http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi) used to predict hairpin structures from the flanking 200 nt sequences. Differential expression analysis of miRNAs (DE-miRNAs) (p-value < 0.05) was conducted using edgeR. PsRNATarget [38] (https://www.zhaolab.org/psRNATarget/) was employed to predict miRNA target genes regulated at the transcriptional or post-translational level. CleaveLand 4 [39] was utilized to identify potential miRNA editing sites using degradome sequencing data, and t-plots were constructed for efficient analysis of potential miRNA targets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of DE-mRNAs and target genes were performed using the web tools agriGO [40] (http://systemsbiology.cau.edu.cn/agriGOv2/) and KOBAS [41] (http://bioinfo.org/kobas), respectively.

Construction of DE-lncRNA-miRNA-mRNA regulatory pathway and sequence alignment

Based on the FPKM score and the dynamic trends observed throughout the infection period in RNA-seq data, significant DE-lncRNAs were selected for further analysis. PsRNATarget was employed to predict the target miRNAs of DE-lncRNAs. The subcellular localization of lncRNAs was predicted using lncLocator [42] (http://www.csbio.sjtu.edu.cn/bioinf/lncLocator/) and iLoc-LncRNA [43] (http://lin-group.cn/server/iLoc-LncRNA/home.php), meanwhile RNAfold was utilized to predict the secondary structure of lncRNAs. Sequence alignments of lncRNAs were analyzed using MEGA 11 [44] and GeneDoc v2.7 [45], and phylogenetic trees were constructed using neighbor joining (NJ) with 1000 bootstrap replicates. The sRNAminer [46] tool was utilized to visualize the location and characteristics of lncRNAs in the genome. Finally, logo sequence diagrams were generated using WebLogo [47] (https://weblogo.berkeley.edu/logo.cgi).

The datasets presented for sequence alignments can be accessed from the NCBI database. The sequence of SllncRNA15942 is from BioProject PRJNA119, BioSample SAMN02981290 and PtlncRNA1232 comes from BioProject PRJNA17973, BioSample SAMN02953657. The accession number of TAS3s are: NR_143941 (AtTAS3), NR_138079 (SlTAS3-1), EU293143.1 (OsTAS3a1), and MK511448.1 (HtTAS3c).

Gene co-expression network analysis and module identification

The co-expression gene network was constructed using the WGCNA [48] package in R software [49], utilizing the expression values (FPKM) obtained from RNA-seq data. Prior to conducting cluster analysis, samples with low correlation were excluded. For this WGCNA analysis, a platform threshold line of 0.85 and a β value of 1 were set. Additionally, the module similarity threshold was set to 0.25, with an expression threshold of 1, and a minimum of 30 genes per module for partitioning (Additional file 1: Figure S1). Our target lncRNAs, miRNAs, and mRNAs were identified within the obtained results and assigned to specific modules based on gene significance, margins, and node information within the modules. Finally, the summarized information was visualized using Cytoscape v3.10.1 software [50].

Real-time qRT-PCR and relative expression calculation

In inoculated samples at divergent stages, transcriptional levels of ncRNAs and representative genes were measured by real-time quantitative reverse transcription PCR (qRT-PCR), respectively. Three independent experimental replicates for each treatment were carried out. First-strand cDNA was synthesized from 2 µg DNase-treated total RNA (Thermal reversed first-strand cDNA synthesis Kit) and 50 ng cDNA was taken as template for PCR. Relative expression (RE) was calculated using the 2−ΔΔCT method. Primers (Additional file 2: Table S1) were produced using the Primer3Plus website (https://www.primer3plus.com/) [51] with general settings: Product Size Ranges were set to 50–200, Primer Size was configured as Min: 17, Opt: 20, Max: 25; Primer Tm was set to Min: 55.0, Opt: 58.0, Max: 60.0; and all other settings were left at their default parameters. Hb18S gene was used as an internal reference gene to normalize the variations during the experimental process, and the transcripts of HblncRNA29219, HbTAS3-1, HbARF2/3/4 were conducted. For the detection of relative expression levels of tasiRNAs (hbr-tasiRNA-1, hbr-tasiRNA-2a and hbr-tasiRNA-2b), a tailing-based reverse transcription kit was used, with hbr-U6 serving as the reference gene.

Results

Genomic and regulatory landscape of DE-lncRNAs in response to anthracnose stress

Following a comprehensive RNA sequencing analysis, we detected 350 DE-lncRNAs. Our focus initially turned to their chromosomal distribution, revealing that 338 DE-lncRNAs span 18 chromosomes, with an additional 12 situated on unplaced genomic scaffolds (Fig. 1A). Notably, a significant subset of these DE-lncRNAs clustered near the telomeres of chromosomes, coinciding with regions of heightened gene density, highlighting the potential regulatory importance of these gene-rich domains.

Fig. 1
figure 1

Chromosomal Localization and Co-expression Modules Detection. (A) Chromosomal locations of DE-lncRNAs in H. Brasiliensis. Chromosome length (Mb) is indicated on the left scale, with green stars representing DE-lncRNAs. Chromosomes are color-coded based on gene density, where red denotes highest density and blue denotes lowest density. (B) Gene modules identified by WGCNA in H. Brasiliensis leaves. Eight modules were identified using eigengene calculation and are color-coded accordingly. The ‘Grey’ module represents unassigned genes. (C) Correlation coefficients and significance between gene expression modules and different time stages of H. Brasiliensis in response to anthracnose infection. The heatmap indicates the intensity and direction of correlations (red, positive; blue, negative), with corresponding legends provided on the right side

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Utilizing whole-genome expression profiles, we employed WGCNA to uncover modules and interaction networks linked to resistance, selectively excluding genes with expression levels below 1 across samples. Employing a dynamic tree cutting approach, we amalgamated modules with similar expression profiles, identifying 8 distinct clusters (Fig. 1B and C).

Within the turquoise module, we pinpointed genes pertinent to DE-lncRNAs, visualizing key nodes using Cytoscape software (Additional file 2: Table S2). This analysis unveiled intricate interconnections among DE-lncRNAs, miRNAs, and mRNAs, highlighting a regulatory pathway implicating these molecules in anthracnose resistance in rubber tree leaves. Moreover, the identification of pivotal genes within this co-expression network holds promise for advancing our understanding of anthracnose resistance mechanisms in H.brasiliensis.

Elucidating the construction of a ceRNA regulatory network in response to pathogenic infection

The ceRNA hypothesis posits a dynamic interplay among diverse RNA species within cells, including mRNA, miRNA, and lncRNA, as they compete for shared miRNA binding sites, thereby orchestrating a complex network of reciprocal interactions [52]. According to this hypothesis, RNA molecules harboring overlapping miRNA binding sites engage in competitive binding, thereby intricately influencing each other’s expression levels and subsequently regulating gene expression and cellular processes. In this study, we systematically analyzed differential expression profiles of lncRNAs, miRNAs, and mRNAs to meticulously construct an expansive ceRNA regulatory network. Emphasizing miRNAs previously implicated in resistance mechanisms, our synthesis culminates in Fig. 2, delineating the involvement of 11 lncRNAs, 7 miRNAs, and 62 mRNAs (Additional file 2: Table S3), all responsive to the challenges posed by Colletotrichum gloeosporioides infection.

Fig. 2
figure 2

ceRNA Network Diagram Related to Disease Resistance in Rubber Trees

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Within our ceRNA networks, HblncRNA25176 is predicted to sequester miR160a, subsequently modulating the expression of aminotransferase (XM_058136915.1), pivotal in amino acid metabolism. Similarly, HblncRNA5677 targets miR165a-5p_2, regulating the ethylene-responsive transcription factor ERF113-like (XM_021782035.2) crucial in ethylene signaling. Additionally, HblncRNA1946 targets miR165a-5p_2, thus influencing glucan endo-1,3-beta-glucosidase (XM_021818339.2) involved in β-1,3-glucan degradation within the pathogen cell wall, alongside allene oxide cyclase, chloroplastic-like (XM_021796346.2), pivotal in jasmonic acid biosynthesis.

Further interactions are seen with HblncRNA9142448 and lncRNA2492356 binding to miR165-5p_L, thereby regulating acyl-lipid (9 − 3)-desaturase-like (XM_021781806.2) linked to lipid metabolism, and biotin carboxyl carrier protein of acetyl-CoA carboxylase 1, chloroplastic-like isoform X1 (XM_058149554.1), central to fatty acid metabolism. Moreover, HblncRNA20893 binds to miR166a-3p_1, targeting subtilisin-like protease SBT1.8 (XM_021804130.2), pivotal in pathogen resistance and plant immunity. Additionally, HblncRNA39189 targets miR394a, influencing F-box only protein 6 (XM_021784685.2) involved in signal transduction, and ABC transporter G family member 31 (XM_021810554.2), facilitating substance transport across membranes. HblncRNA249634 targets miR394a-p3, indirectly modulating Auxin transporter-like protein 5 isoform X1 (XM_058141406.1) and Serine/threonine-protein kinase BRI1-like 2 (XM_058153334.1), pivotal in plant hormone signaling. Moreover, HblncRNA2497138, HblncRNA13153, and HblncRNA9146131 interact with miR6445b-p3_2, thereby respectively regulating receptor protein kinase probable LRR receptor-like serine/threonine-protein kinase At1g51820 isoform X3 (XM_058149728.1), ABC transporter G family member 39-like (XM_021802261.2), pivotal in compound transport, and gibberellin 2-beta-dioxygenase 8-like (XM_058140618.1), crucial in gibberellin metabolism. Collectively, these findings underscore the intricate stress-induced regulatory mechanisms activated in rubber trees following C.gloeosporioides infection. They highlight the pervasive regulatory roles of lncRNAs in modulating mRNA expression through intricate miRNA-mediated interactions within the cellular milieu.

Functional crosstalk between HblncRNA29219 and antisense-located hbr-miR482a

During our integrated analysis of lncRNAs and miRNAs sequencing data, an intriguing phenomenon drew our attention. Positioned on chromosome 13 of the rubber tree genome, HblncRNA29219 harbors two miRNAs, hbr-pre-miR482a and hbr-pre-miR482a-3p, on its antisense strand (Fig. 3A). Considering prior studies suggesting that lncRNAs may function by suppressing antisense gene expression [53], we examined the stress-induced expression patterns of HblncRNA29219 and the miRNAs located on its antisense strand following Colletotrichum infection. Our observations revealed upregulation of HblncRNA29219 and concomitant downregulation of miR482a, while hbr-miR482a-p3 from the 3’ end maintained stable expression throughout the infection (Fig. 3B and C). These findings led us to hypothesize that during Colletotrichum infection, HblncRNA29219 may modulate the expression of antisense strand hbr-miR482a without affecting hbr-miR482a-p3.

Fig. 3
figure 3

Mechanism of HblncRNA29219 and its Antisense Chain hbr-miR482 in Rubber Trees under Fungal Infection. (A) Genomic locations of HblncRNA29219 and its antisense chain hbr-miR482 identified from transcriptome sequencing of Brazilian rubber trees. (B) Positioning of the two arm ends of hbr-miR482 within the pre-miR482 sequence of rubber trees. (C) Expression levels of HblncRNA29219, hbr-miR482, and hbr-miR482-p3 at different time points after C.gloeosporioides infection in rubber trees. qRT-PCR analysis of relative expressions of HblncRNA29219 in rubber tree leaves infected with anthracnose at different stages. Error bars represent mean ± standard deviation (SD). (D) Secondary structure prediction of HblncRNA29219 in rubber trees and its comparative positions with hbr-pre-miR482, alongside peak maps of both molecules. (E) Multiple sequence alignment of pre-miR482 sequences from rubber trees, tomatoes, and P.tomentosa, highlighting the mature miR482 sequence. (F) Multiple sequence alignment of lncRNAs from rubber trees and tomatoes, indicating positions where these lncRNAs can match the mature miR482 sequence. (G) Full sequence alignment of miR482 from rubber trees and tomatoes with the antisense lncRNA. (H) Changes in expression levels of mRNA targeted by hbr-miR482 after C.gloeosporioides infection in rubber trees. (I) WGCNA highlighting co-expression of HblncRNA29219, hbr-miR482a, hbr-miR482-p3, and HbDANJ10 within the same module. (J) Action module of HblncRNA29219-miR482-HbDANJ10 related to disease resistance in rubber trees

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To explore how HblncRNA29219 potentially influences the expression of antisense strand hbr-miR482a, we initially computed the spatial interaction energy between HblncRNA29219 and hbr-pre-miR482a. Computational analysis revealed robust interaction stability in the spatial domain (Energy = -230.76 kcal/mol). Furthermore, secondary structure predictions of HblncRNA29219 and hbr-pre-miR482a highlighted a conservatively stable structure in the binding region, suggesting functional potential therein (Fig. 3D). Sequence alignment with pre-miR482a from other species, including tomato and poplar, showed significant conservation (Fig. 3E), reinforcing the notion that lncRNAs serving as miRNA precursors exhibit positively correlated expression changes [54, 55]. Alignments across rubber tree, tomato, and poplar sequences revealed notable conservation in the 400–500 bp region of HblncRNA29219, particularly within the binding site (Fig. 3F).

Drawing insights from tomato research, where experimental evidence supports sly-miR482a cleaving the antisense strand lncRNA under fungal infection [56], we postulate a similar mechanism in the rubber tree. Given that hbr-pre-miR482a resides on HblncRNA29219’s antisense strand, mature hbr-miR482a likely binds through complementary base pairing and cleaves HblncRNA29219. PsRNAtarget analysis corroborated a mechanism akin to that observed in tomato (Fig. 3G). Consequently, under fungal stress, HblncRNA29219 exhibits elevated expression, resulting in decreased mature hbr-miR482a levels upon binding (Fig. 3B and C). Using the Hb18S gene as a reference gene to conduct qRT-PCR experiments, we validated the expression of HblncRNA29219 under fungal stress (Fig. 3C. Furthermore, reduced mature hbr-miR482a expression enhances the expression of its target gene HbDANJ (Fig. 3H), known to positively regulate plant resistance to fungi [57], thereby bolstering rubber tree leaf resistance to Colletotrichum infection. Through weighted gene co expression network analysis, the results showed that all of our sequences are within the same module (Fig. 3I), indicating that they can jointly participate in regulation.

In summary, our study elucidates the complex regulatory interplay between lncRNAs and miRNAs in rubber trees, akin to mechanisms observed in tomato. The mechanism we studied is shown in Fig. 3J, the upper part is the biogenic pathway of miRNA, which generates primary miRNA (pri-miRNA) through transcription, and then processes pri miRNA into precursor miRNA (pre-miRNA), which is finally cleaved to generate mature miRNA. We demonstrate that HblncRNA29219 regulates the expression of hbr-miR482a, thereby influencing downstream HbDANJ expression and modulating rubber tree leaf resistance to Colletotrichum. Throughout this process, HblncRNA29219 exhibits a negative correlation with hbr-miR482a, which in turn negatively regulates the stress response of rubber trees to Colletotrichum (Fig. 3J). These discoveries provide insights into the intricate regulatory networks governing plant-microbe interactions and highlight the role of lncRNAs in shaping plant defense responses.

MiR390-TAS3-ARF pathway mediating the response to anthracnose disease stress in H. Brasiliensis

The miR390-TAS3-ARF pathway represents a fundamental regulatory axis in plant biology, crucial for growth, development [58], and adeptness in responding to diverse stressors such as disease and salinity [59]. MiR390 initiates the production of trans-acting small interfering RNAs (tasiRNAs) from TAS3 (TRANS-ACTING SIRNA GENE3) transcripts, pivotal in modulating ARF (AUXIN RESPONSE FACTOR) genes central to auxin signaling, thus exerting profound influence over plant physiology and stress responses. Nevertheless, comprehensive documentation regarding TAS sequences, the miR390-TAS3-ARF pathway, and its role in disease resistance mechanisms in H. brasiliensis has been notably scarce. Our integrative analysis, incorporating multi-omics data and whole-transcriptome investigations of the rubber tree, has now unveiled this intricate regulatory landscape.

During our examination of DE-lncRNAs, we observed significant variations in the expression of HblncRNA7430 after anthracnose infection of rubber tree leaves, revealing discernible regulatory trends as the infection progressed (Fig. 4A). This finding strongly suggests the involvement of HblncRNA7430 in critical regulatory pathways. Subsequent computational projections for potential target miRNAs and mRNAs of HblncRNA7430, through joint analyses using psRNATarget and psRobot, identified miR390 as the primary miRNA interacting with HblncRNA7430, featuring dual binding sites (Fig. 4B). According to psRNATarget computations, the proximal site near the 5’ end showed translational repression due to seed sequence mismatches, whereas the site near the 3’ end facilitated cleavage and degradation by miR390. This intriguing observation aligns closely with the ‘two-hit’ model characterizing the miR390-TAS3-ARF mechanism in H. brasiliensis.

Fig. 4
figure 4

Comprehensive Analysis of the Mechanism of HblncR7430 in Rubber Tree Leaves upon Anthracnose Infection. (A) FPKM (Fragments Per Kilobase Million) values of HbTAS3-1 in RNA-seq data from rubber tree leaves infected with anthracnose at different stages. (B) Specific binding regions between HbTAS3 transcripts and miR390. (C) FPKM values of HbTAS3-2 and HbTAS3-3 in RNA-seq data from rubber tree leaves infected with anthracnose at different stages. (D) Secondary structure (MEF) and mountain plot of HbTAS3-1, highlighting the complementary regions with miR390 (circled and enlarged). (E) Multiple sequence alignment of TAS3 transcripts from Arabidopsis, tomato, rice, and Jerusalem artichoke, with a phylogenetic tree constructed using the NJ-method with 1000 bootstrap replicates. The conserved binding sites between TAS3 and miR390 are marked by a red box, while the regions for tasiRNA production are marked by a blue box. (F) sRNA-generating loci of HbTAS3-1 transcript and conservation of tasiRNAs. Abundance of sRNAs is color-coded: cyan for 21-nt reads, green for 22-nt, purple for 23-nt, orange for 24-nt, and grey for others. Phasing scores of tasiRNAs are shown in a line plot, with peaks indicating loci for tasiRNA production. Sequence logo represents conservation of tasiRNAs derived from various land plants. (G) T-plots of tasiRNA-ARF pairs confirmed by degradome sequencing in H. Brasiliensis. Red dots indicate abundance and cleavage sites of HbARF transcripts targeted by tasiRNAs. (H) (q)RT-PCR analysis of relative expressions of HbTAS3-1, HbARF2B, HbARF3, and HbARF4 transcripts in rubber tree leaves infected with anthracnose at different stages. Error bars represent mean ± standard deviation (SD). (I) Co-expression network in the turquoise module of miR390-TAS3-ARF module and their corresponding genes. Different shapes and colors represent miRNA, lncRNA, and mRNA. (J) MiR390-TAS3-ARF regulatory pathway in rubber tree

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In plants, miR390 is known to associate with the AGO7 protein within the RISC, binding lncRNAs at two sites of base complementarity. The site near the 3’ end of the lncRNA exhibits complete complementarity, undergoing cleavage by RISC, whereas the site near the 5’ end, with preserved mismatches, avoids cleavage. The cleaved lncRNA strand subsequently undergoes enzymatic action by SGS3 and RDR6, generating double-stranded RNA, which is processed into siRNA by DCL4, targeting downstream sequences [60, 61].The regions targeted by miR390 at these positions correspond to TAS3 sequences, resembling characteristics observed in the investigated lncRNA. Hence, we hypothesize that HblncRNA7430 represents a TAS3 sequence in the rubber tree (HbTAS3-1), substantiating the presence of the miR390-TAS3-ARF mechanism in H. brasiliensis and its role in the disease resistance system.

Employing computational tools, we thoroughly scanned the entire genome of rubber tree lncRNA transcripts, identifying two additional potential TAS3 lncRNA transcripts, despite their insignificant expression levels during infection (Fig. 4C; HblncRNA35225.1: HbTAS3-2; HblncRNA35225.2: HbTAS3-3). The two lncRNA transcripts exhibited dual-binding capabilities with miR390 as well (Fig. 4B). Furthermore, secondary structure predictions unveiled stable spatial conformations in these lncRNA sequences, particularly at miR390 binding sites, characterized by lower free energy (Fig. 4D).

Additionally, TAS3 transcripts from Arabidopsis, tomato, rice [62], and Jerusalem artichoke [63] were accessed via public databases for multiple sequence alignments and phylogenetic analyses with the HbTAS3 sequences (Fig. 4E; Neighbor-Joining Algorithm, bootstrap 1000). Results indicated close phylogenetic proximity between HbTAS3s and the rice TAS3 sequence, supporting robust topological structures. Notably, multiple sequence alignment revealed a highly conserved region spanning approximately 40nt between the two miR390 binding sites, identified as the locus for tasiRNA production, yielding two tasiRNAs sequentially. To validate this hypothesis, sRNAminer was utilized to map small RNA results, demonstrating predominant 21nt small RNAs at the HbTAS3-1 locus, marked by a PHAS locus with interloci spacing consistent with anticipated tasiRNA lengths (Fig. 4F).

Furthermore, employing tasiRNA sequences from various species (tasiRNA-1 and tasiRNA-2; see Additional file 2: Table S4), we illustrated tasiRNA base compositions via sequence logos. Moreover, based on multiple sequence alignments, the TAS3 sequence in the rubber tree was shown capable of generating three tasiRNA types: tasiRNA-1 (sequence: 5’-TTCTTGACCTTGTAAGACCTT-3’), tasiRNA-2a (sequence: 5’- TTTTTAATCTTATAAGACCCC-3’), and tasiRNA-2b (sequence: 5’-TTCTTGACCTTGTAAGACCCT-3’), aligning their base sequences with the characteristics displayed in the sequence logo. Additionally, subcellular localization predictions of the HbTAS3 sequences (Additional file 2: Table S5) suggested likely cytoplasmic functionality for these lncRNAs, implicated in transcriptional or post-transcriptional regulation, mirroring reported mechanisms in other plant species.

Collectively, these findings provide compelling evidence for the miR390-TAS3-ARF mechanism in H. brasiliensis, underscoring its significance in disease resistance. In-depth exploration using psRNATarget confirmed that rubber tree tasiRNAs effectively cleave ARF2/ARF3/ARF4 transcripts for degradation and translational inhibition, thereby modulating their expression (Additional file 2: Table S6). Further validation through degradome sequencing data and t-plots demonstrated efficient cleavage of ARF2/ARF3/ARF4 transcripts by rubber tree tasiRNAs (Fig. 4G). Remarkably, among the three identified HbTAS3 sequences, only HbTAS3-1 exhibited elevated expression and significant upregulation under stress conditions following anthracnose infection of rubber tree leaves, as validated by qRT-PCR experiments consistent with RNA-seq data (Fig. 4H). Meanwhile, we demonstrated a high degree of consistency between the expression of HbTAS3-1 and hbr-tasiRNA-1 (the main tasiRNA produced by HbTAS3-1) during the infection process. The relative expression levels of hbr-tasiRNA-1 are provided in Additional file 1: Figure S2. In addition, we directly calculated the correlation between HbTAS3-1 and hbr-tasiRNA-1 expression levels, finding a strong correlation with ρ = 0.79 (Additional file 1: Figure S3). Further, this stress-induced upregulation of HbTAS3-1 in the rubber tree leads to increased production of tasiRNAs targeting ARF2/ARF3/ARF4 transcripts, corroborating regulatory pathways identified in RNA-seq data. Additionally, qRT-PCR analysis validated downregulation of ARF2/ARF3/ARF4 transcripts expression under stress conditions concurrent with HbTAS3-1 upregulation (Fig. 4H), highlighting their interdependent regulatory dynamics. Concurrently, based on the modular data outcomes of WGCNA result, HbTAS3-1 and miR390 were identified to reside within the turquoise module, which exhibits a high correlation with disease resistance, and they are interconnected by shared hub genes (see Table S7 in Additional file 2), thus jointly participating in the response to anthracnose infection (Fig. 4I).

In essence, our study unveils and thoroughly investigates the miR390-TAS3-ARF mechanism in H. brasiliensis (Fig. 4J), where anthracnose fungus infection of rubber tree leaves induces stress-related upregulation of HbTAS3-1. This prompts miR390 to form a RISC complex with AGO7 protein, binding complementarily to HbTAS3-1 at dual target sites. Cleavage by RISC at the fully complementary site near the 3’ end of HbTAS3-1 generates a cleaved lncRNA strand, subsequently processed enzymatically into double-stranded RNA, further cleaved into tasiRNA by corresponding enzymes. These tasiRNAs target downstream ARF2/ARF3/ARF4 sequences, effectuating their downregulation and pivotal roles in the plant’s stress response and disease resistance mechanism.

Discussion

In this study, we employed a multi-omics approach to elucidate the intricate regulatory mechanisms involving lncRNAs, miRNAs, and mRNAs in H. brasiliensis under anthracnose stress. Our findings unveil novel aspects of lncRNA-mediated regulation, highlighting its implications for plant defense responses and emphasizing the complexity of regulatory networks in rubber tree leaves.

Central to our investigation was the ceRNA hypothesis, positing that lncRNAs competitively bind miRNAs to modulate mRNA expression, thereby influencing cellular responses to stress. Through meticulous analysis of lncRNA-miRNA-mRNA interactions, we constructed an expansive ceRNA regulatory network responsive to C.gloeosporioides infection. In recent years, examination of the landscape of ceRNA mechanisms across diverse species has emphasized their pivotal roles in gene regulatory networks. Upon this, human research has established that ceRNAs, including lncRNAs and circRNAs, function as miRNA sponges to modulate mRNA expression, particularly in disease contexts [64]. Similarly, studies in murine models have extensively explored the versatility of ceRNA mechanisms, using transgenic and gene knockout technologies to elucidate their contributions to disease processes [65]. In Drosophila melanogaster, a classic model for developmental biology, investigations into ceRNA mechanisms predominantly focus on developmental regulation, revealing intricate ceRNA networks that orchestrate developmental processes [66]. Conversely, research into ceRNA mechanisms in plants, particularly in economically important crops, remains in its nascent stages. Emerging studies underscore the potential of plant ceRNAs in regulating growth [67], development [68], and stress responses [69,70,71] through competitive interactions with miRNAs, highlighting the evolutionary conservation and adaptive significance of these mechanisms in plant biology. Advancements in high-throughput sequencing technologies and computational modeling promise to accelerate our understanding of ceRNA networks across species, suggesting future research directions should prioritize unraveling the specific roles of individual ceRNAs in diverse biological contexts, elucidating their evolutionary trajectories, and exploring their therapeutic and agricultural applications.

A notable discovery from our investigation was the functional crosstalk between HblncRNA29219 and antisense-located hbr-miR482a in response to Colletotrichum infection. HblncRNA29219, harboring hbr-pre-miR482a on its antisense strand, exhibited stress-induced upregulation inversely correlated with mature hbr-miR482a levels. Computational and experimental evidence suggested that HblncRNA29219 modulates the expression of hbr-miR482a, thereby influencing downstream HbDANJ expression critical for fungal resistance in rubber tree leaves. These findings provide mechanistic insights into how lncRNAs regulate miRNA expression dynamics under stress conditions, shaping plant defense responses against pathogens. Research on the regulation of antisense miRNA gene expression by lncRNAs in plants is expanding [56, 70], akin to the mechanisms explored in our study on HblncRNA29219 and antisense-located hbr-miR482a, underscoring the diversity and complexity of lncRNA-mediated regulation of antisense miRNA gene expression in plants.

Furthermore, our study identified and characterized the miR390-TAS3-ARF pathway as a pivotal regulatory axis in H. brasiliensis during anthracnose stress. Through integrated analyses of HblncRNA7430 and computational predictions, we demonstrated its role as a TAS3 sequence interacting with miR390 to produce tasiRNAs. These tasiRNAs target ARF genes, modulating auxin signaling and contributing to disease resistance mechanisms in rubber trees. The stress-induced upregulation of HbTAS3-1 and subsequent tasiRNA production validated our hypothesis, highlighting the functional conservation and adaptive significance of the miR390-TAS3-ARF pathway in plant stress responses. miR390-TAS3-ARF is an important regulatory system widely present in plants, playing crucial roles in their development and growth. In recent years, research on various species has deepened our understanding of this regulatory system. For instance, in rice, it regulates rice growth and development [72]; in maize, it controls leaf morphology development and stress responses [73, 74]; in wheat, it plays a key role in nutrient uptake by roots and adaptation to adverse conditions [75, 76]. Besides, it may have the potential to improve the salt tolerance of Jerusalem artichoke under salt stress [63]. Overall, our research and studies in other species indicate the conservation and functional diversity of the miR390-TAS3-ARF system across different plant species, providing important clues for further understanding the evolution and adaptive significance of this crucial regulatory mechanism in plants.

In conclusion, our study unravels complex regulatory networks involving lncRNAs, miRNAs, and mRNAs in H. brasiliensis under anthracnose stress. These findings underscore the regulatory versatility of lncRNAs in shaping gene expression dynamics critical for plant defense responses. Future investigations could delve deeper into the evolutionary conservation and functional diversity of lncRNA-mediated regulatory pathways across different plant species and stress conditions. Furthermore, exploring the translational potential of these insights could pave the way for developing novel strategies to enhance disease resistance in economically significant crops.

Conclusion

In conclusion, we employed a multi-omics approach integrating lncRNA, miRNA, and degradome sequencing to elucidate the intricate regulatory landscape of ncRNAs in Hevea brasiliensis under anthracnose stress. Through differential expression analysis of transcriptome sequencing data, we identified 350 DE-lncRNAs dispersed across the genome, with notable accumulations proximal to telomeres, suggesting their potential roles in stress adaptation and genomic stability. Our investigation revealed a complex ceRNA network comprising 11 lncRNAs, 7 miRNAs, and 62 mRNAs, which serve as pivotal regulatory hubs that modulate essential plant defense pathways. Furthermore, we investigated the functional significance of HblncRNA29219, its antisense counterpart hbr-miR482a, and the miR390-TAS3-ARF pathway in augmenting anthracnose resistance in rubber trees, providing valuable insights into complex plant‒microbe interactions and promising avenues for the development of durable crop protection strategies. These findings not only deepen our understanding of the molecular mechanisms underlying plant resistance but also pave the way for targeted genetic interventions aimed at enhancing crop resilience against devastating diseases.

Data availability

The datasets supporting the conclusions of this article are included within the article and its additional files. Sequence data that support the findings of this study have been deposited in the NCBI Sequence Read Archive (SRA) with the primary accession code PRJNA1144109. The data will be publicly released on March 10, 2025.

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Acknowledgements

We are grateful to our team members for their valuable discussions and inspiration.

Funding

This work was supported by the Hainan Provincial Natural Science Foundation of China (321RC456), the National Natural Science Foundation of China (31960299), the National Natural Science Foundation of China (32060591, 32260716) and the startup fund from Hainan University for YHY.

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Author notes

  1. Yanluo Zeng and Tianbin Guo contributed equally to this work.

Authors and Affiliations

  1. School of Tropical Agriculture and Forestry, Hainan University, Haikou, Hainan, China

    Yanluo Zeng, Tianbin Guo & Hongyan Yin

  2. School of Breeding and Multiplication, Hainan University, Haikou, Hainan, China

    Liping Feng & Hongli Luo

  3. TJ-YZ School of Network Science, Haikou University of Economics, Haikou, China

    Zhuoda Yin

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  1. Yanluo Zeng
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HY and HL conceived the project, designed, and supervised this study. YZ and TG conducted comprehensive analyses on RNA-seq, sRNA-seq, and degradome data, as well as the exploration and prediction of the regulatory mechanisms involving lncRNA-miRNA-mRNA. LF carried out qRT-PCR experiments and analyzed the resulting data. YZ, TG and HY wrote the manuscript. All authors reviewed and approved the final manuscript.

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Correspondence to Hongli Luo or Hongyan Yin.

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13007_2024_1301_MOESM1_ESM.pdf

Additional file 1: Figure S1. Soft threshold powers for scale independence and mean connectivity in WGCNA analysis. Figure S2. Relative expression of hbr-tasiRNA-1 in qRT-PCR. Figure S3. Correlation scatter plot of HbTAS3-1 and hbr-tasiRNA-1 (Spearman rho = 0.77). The red trend line is based on linear fitting of data points and does not directly represent Spearman correlation

13007_2024_1301_MOESM2_ESM.xls

Additional file 2: Table S1. All primers of this study. Table S2. The gene list of WGCNA turquoise module (the bold IDs are lncRNAs). Table S3. The ceRNA networks in Hevea brasiliensis. Table S4. The sequence of tasiRNAs from land plants. Table S5. Subcellular localization results of the HbTAS3 sequence. Table S6. The psRNATarget results of tasiRNA targets among HbARF transcripts. Table S7. The hub genes interconnected by HbTAS3-1 and hbr-miR390

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Zeng, Y., Guo, T., Feng, L. et al. Insights into lncRNA-mediated regulatory networks in Hevea brasiliensis under anthracnose stress. Plant Methods 20, 182 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13007-024-01301-4

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13007-024-01301-4

Keywords

Plant Methods

ISSN: 1746-4811