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Article

Characterization of Drought-Responsive miRNAs in Peanut Through Integrated Transcriptomic Approaches

by
Xin Zhang
1,
Rui Zhang
2,
Zhenbo Chen
2,
Xiaoyu Zhang
3,
Xiaoji Zhang
2,
Yuexia Tian
1,
Yunyun Xue
1,
Huiqi Zhang
1,
Na Li
1 and
Dongmei Bai
1,2,*
1
Institute of Industrial Crops, Shanxi Agricultural University, Taiyuan 030031, China
2
College of Agronomy, Shanxi Agricultural University, Taigu 030801, China
3
College of Plant Protection, Shanxi Agricultural University, Taigu 030801, China
*
Author to whom correspondence should be addressed.
Agriculture 2025, 15(21), 2190; https://doi.org/10.3390/agriculture15212190
Submission received: 17 September 2025 / Revised: 15 October 2025 / Accepted: 21 October 2025 / Published: 22 October 2025
(This article belongs to the Topic Tolerance to Drought and Salt Stress in Plants, 2nd volume)
Browse Figures
Review Reports Versions Notes

Abstract

Drought stress severely limits peanut productivity, highlighting the urgent need to understand the molecular mechanisms that underlie drought adaptation. While microRNAs (miRNAs) are known to play essential roles in plant stress responses, their functional contributions in polyploid crops like peanut remain insufficiently explored. This study provides the first integrated transcriptomic analysis of drought-responsive miRNAs in tetraploid peanut (Arachis hypogaea). We performed high-throughput sRNA sequencing on a drought-tolerant cultivar Fenhua 8 under PEG6000-simulated drought stress, identifying 10 conserved drought-responsive miRNAs. Among these, ahy-miR398 and ahy-miR408 were significantly downregulated under drought conditions. Degradome sequencing revealed that ahy-miR398 targets copper chaperones for superoxide dismutase (CCSs), potentially reducing SOD activation and amplifying oxidative stress. In contrast, ahy-miR408 targets laccase 12 (LAC12), P-type ATPase copper transporters (COPAs), and a blue copper protein-like (PCL) gene. These targets are involved in copper homeostasis and the regulation of reactive oxygen species (ROS), suggesting that ahy-miR408 plays a role in oxidative stress management. Functional validation in transgenic Arabidopsis lines overexpressing ahy-miR398 or ahy-miR408 showed significantly reduced drought tolerance, with impaired seed germination, shorter primary roots, and exacerbated growth suppression during water deprivation. Taken together, these findings highlight a novel miRNA-mediated regulatory network in peanut drought adaptation, centered on copper-associated oxidative stress management. This study provides new insights into miRNA-based regulation in polyploid crops and offers potential molecular targets for breeding climate-resilient peanut varieties, especially in arid regions where yield stability is crucial.

1. Introduction

Drought stress, a core challenge to global agricultural sustainability, can reduce yields of major crops by 30–60% through mechanisms including the disruption of photosynthesis, oxidative damage induction, and osmotic regulation disturbance [1,2]. Peanut (Arachis hypogaea L.), as the world’s fourth largest oilseed crop with an annual production of 45 million tons (accounting for 12% of global vegetable oil supply), has 60% of its cultivation areas distributed in arid/semi-arid regions receiving less than 600 mm of annual rainfall [3]. Drought-induced pod abortion in peanut can cause up to 58% yield reductions under severe water deficits (when the soil water content decreases to 40% of the field capacity) [4]. These yield fluctuations not only threaten food security (approximately 800 million people worldwide rely on peanut as a primary protein source [5]) but also highlight the strategic significance of deciphering drought resistance mechanisms. Plants activate multi-layered regulatory networks under drought stress, among which post-transcriptional regulation—particularly microRNA (miRNA)-mediated gene silencing—has emerged as a research focus in stress biology due to its rapid response and precise regulatory characteristics [6].
MicroRNAs (miRNAs), 20–24 nt non-coding RNAs, have emerged as pivotal post-transcriptional regulators in plant stress adaptation through Argonaute protein-complex-mediated mRNA cleavage or translational repression [7]. Unlike transcriptional regulators exhibiting 2–4 h response delays, miRNAs demonstrate rapid stress perception, activating within 30 min, and precise targeting, with each miRNA regulating 3–5 genes [7]. Their spatiotemporal expression dynamics coordinate drought resilience through modulating ROS homeostasis, such as in Arabidopsis, where miR398-mediated suppression of CSD1/2 reduces oxidative damage; optimizing hydraulic architecture, as seen in rice, where miR408 enhances xylem lignification to improve embolism resistance; and fine-tuning ABA signaling, exemplified by barley miR159a, which regulates stomatal closure [8,9,10]. Functional genomics studies in model plants have demonstrated the crucial role of miRNAs in drought resilience through distinct molecular mechanisms. For instance, miR393a overexpression enhances drought tolerance by modulating auxin signaling pathways in Creeping bentgrass (Agrostis stolonifera L.) [11]. In Populus species, miR169o improves drought resistance through precise regulation of its target gene PtNF-YA6, which participates in stress-responsive transcriptional networks [12]. The miR172d/PuGTL1-PuSDD1 regulatory module in poplar exemplifies dual functional optimization, simultaneously suppressing stomatal development via PuGTL1 inhibition while activating water-use efficiency (WUE) mechanisms through SDD1 induction [13]. These findings position miRNAs as prime targets for molecular breeding, yet their functional characterization in polyploid crops like peanut remains critically underexplored.
To address these challenges, this study employed an integrated transcriptomic approach in peanut (A. hypogaea L. cv. Fenhua 8) under PEG6000-simulated drought stress to identify drought-responsive miRNAs and their regulatory targets. Our strategy encompassed (1) a tiered time-series design [0 h (baseline), 12/24 h (mock control vs. PEG treatment)] coupled with sRNA-seq to profile the miRNA dynamics; (2) degradome sequencing for high-confidence target identification, followed by qPCR validation of miRNA–target negative regulatory relationships; and (3) heterologous overexpression of candidate miRNAs (ahy-miR398 and ahy-miR408) in Arabidopsis to assess their roles in germination, root architecture, and drought resilience. Mechanistically, ahy-miR408 targets laccase 12 (LAC12), copper transporters (COPAs), and blue copper protein-like (PCL), while ahy-miR398 targets copper chaperones for superoxide dismutase (CCSs). These miRNAs negatively regulate their targets, thereby contributing to drought response. These findings unveil functional divergence of miRNAs in peanut drought adaptation and provide novel targets for molecular-marker-assisted breeding.

2. Materials and Methods

2.1. Plant Materials

The drought-resistant peanut cultivar Fenhua 8, provided by the Institute of Industrial Crops, Shanxi Agricultural University (SXAU, China), underwent surface sterilization in 70% ethanol for one minute, followed by 3–4 rinses with sterile deionized water. Seeds were germinated in humidified Petri dishes and subsequently transferred into Hoagland nutrient solution within a controlled growth chamber, where they were maintained at 27 °C under a 14 h light/10 h dark photoperiod with 70% relative humidity. Drought stress was induced at the four-leaf stage by applying 20% (w/v) PEG6000 solution to six seedlings per experimental group. Employing a hierarchical sampling design, treatment groups (T12, T24) were harvested at 12 h and 24 h post-stress induction alongside time-matched mock controls (M12, M24) under normal cultivation, with additional baseline samples (C0) collected at treatment initiation. Supplementary harvests at critical timepoints (0 h, 12 h, and 24 h) provided whole-seedling material for comprehensive miRNAome sequencing analysis. Each treatment was performed with three biological replicates.

2.2. sRNA Library Construction and DNA Sequencing

RNA degradation and contamination were monitored on 1% agarose gels. RNA purity was checked using the NanoPhotometer® spectrophotometer (IMPLEN, Westlake Village, CA, USA). A total amount of 3 μg total RNA per sample was used as the input material for the small RNA library. Sequencing libraries were generated using NEBNext® Multiplex Small RNA Library Prep Set for Illumina® (NEB, Ipswich, MA, USA) following the manufacturer’s recommendations, and index codes were added to attribute sequences to each sample. Briefly, total RNA was ligated to the RNA 3′ and RNA 5′ adapters, and reverse transcription, followed by PCR, was performed to make cDNA constructs of the small RNAs. PCR products were purified on an 8% polyacrylamide gel (100 V, 80 min). DNA fragments corresponding to 140~160 bp (the length of small non-coding RNA plus the 3′ and 5′ adaptors) were recovered and dissolved in 8 μL of elution buffer. Finally, library quality was assessed on the Agilent Bioanalyzer 2100 system using DNA High Sensitivity Chips. We then performed single-end sequencing (50 bp) on an Illumina HiSeq2500 at the LC-BIO (Novogene Experimental Department, Beijing, China) following the vendor’s recommended protocol.

2.3. Degradome Library Construction and Sequencing

To identify the potential targets, equal amounts of RNA from the baseline samples (C0), treatment group samples (T12, T24), and mock control samples (M12, M24) were mixed together for degradome library construction and deep sequencing. The library was named DS. Through preprocessing, clean tags were generated. Then, clean tags were classified through alignment with GenBank, the Rfam database, and the miRNA database. Next, the reads were mapped to the reference genome (assembly Tifrunner gnm2 (v2), GenBank accession GCF_003086295.3, NCBI Annotation Release 106, downloaded on 12 May 2023). The sense strand of peanut cDNA was used to predict miRNA cleavage sites using the CLeaveLand pipeline [14]. Based on the signature number and the abundance of the cleaved position at each occupied transcript, the cleaved transcripts could be categorized into five categories (0, 1, 2, 3, and 4). Category 0 represents the highest confidence, where the cleavage site has the most abundant tag and a single maximum. Category 1 has multiple sites with the maximum abundance. Categories 2 and 3 correspond to sites with a tag abundance above or below the transcript median, respectively. Category 4 indicates sites supported by only one read. These categories reflect decreasing confidence in miRNA-guided cleavage.

2.4. Northern Blotting Analysis

RNA-blot analyses for miRNAs from the total extracts were performed as described previously [15]. Total RNA was extracted from the samples using TRIzol reagent (Takara, Kyoto, Japan) according to the manufacturer’s specifications. RNA was resolved on a 14% denaturing 8 M urea–PAGE gel and then transferred and chemically crosslinked onto a Hybond N+ membrane (GE Healthcare Life Sciences, Marlborough, MA, USA) using N-(3-Dimethylaminopropyl)-N’-ethylcarbodiimide hydrochloride. miRNA probes were end-labeled with [r-32P] ATP using T4 polynucleotide kinase (New England Biolabs, Ipswich, MA, USA). For internal control experiments, blots were stripped for 20 min 4 times at 80 °C in stripping buffer (0.1 × SSC/0.5% SDS). After detecting no signal, the stripped blots were rehybridized with r-32P ATP-labeled U6 gene fragments to confirm the loading amounts. All signals were normalized to the U6 signals obtained from each blot. The expression levels were quantified using ImageJ, with background subtraction and normalization to the internal control applied as instructed.

2.5. Generation of Transgenic Plants

The precursor miRNA gene MIRNA was cloned from peanut and inserted into our laboratory-modified pCAMBIA1300 overexpression vector via LR clonase II (Invitrogen). Agrobacterium tumefaciens strain GV3101 carrying 35S::MIRNA was used to transform Arabidopsis according to a previously described method [16]. The seeds of transgenic T0 Arabidopsis were first sown onto 1/2 Murashige and Skoog (MS) media, and the normally growing T1 plants were transplanted into nutrient soil. The T1 generation seeds were cultured on the MS media supplemented with 50 mg/L hygromycin. The T2 lines were screened and found to be statistically consistent with the 3:1 segregation ratio. The homozygous T2 progeny were verified via reverse transcription–quantitative polymerase chain reaction (RT-qPCR) assay. Subsequent experiments used representative homozygous T2 progeny for further analysis.

2.6. Drought Stress Treatment

Arabidopsis seeds were planted onto 1/2 MS media containing a concentration of 300 mM mannitol for 5 days, followed by analysis of the seed germination. For root growth analysis, the seeds were sown onto 1/2 MS media supplemented with a concentration of 150 mM mannitol for 7 days. Two-week-old wild-type (WT) and miRNA-overexpressing Arabidopsis specimens were planted in nutrient soil and subjected to 20% (w/v) PEG6000 for 15 days to simulate drought treatment. The fresh weight of the seedlings was assessed. Each treatment was performed with three biological replicates.

3. Results

3.1. High-Throughput Sequencing and Annotation of Peanut sRNAs

To investigate endogenous drought stress-responsive miRNAs, we imposed osmotic stress using 20% (w/v) PEG6000 on Fenhua 8 peanut plants. A hierarchical sampling design was implemented: (1) the treatment group (T) at 12 h (T12) and 24 h (T24) post-treatment and (2) the time-matched mock group (M) (M12, M24) under standard cultivation, with baseline samples 0 h (C0) collected prior to treatment initiation (Figure 1A). Total RNA from each sample was used for sRNA library construction. As shown in Table 1, 8,318,209, 12,456,065, 14,081,185, 11,767,476, and 14,303,309 total reads were generated from each library, representing the different timepoints in C0, M12, M24, T12, and T24, respectively. By mapping to the peanut genome (cultivated peanut, A. hypogaea cv. Tifrunner; http://www.peanutbase.org, accessed on 22 October 2023) (Figure 1B), 8,216,527, 12,298,867, 13,877,512, 11,602,168 and 14,101,597 sRNA reads were obtained, corresponding to 1,800,233, 2,923,698, 2,787,519, 2,625,305, and 3,199,519 unique reads in C0, M12, M24, T12, and T24, respectively. These reads were further searched against the Rfam database (http://xfam.org/, accessed on 22 October 2023) to remove known sRNA, such as ribosomal RNA (rRNA), transfer RNA (tRNA), and small nucleolar RNAs (snoRNAs), and the Repbase database (http://www.girinst.org/repbase/update/index.html, accessed on 22 October 2023) was used to remove repeats. In the end, 4,334,116, 6,818,337, 7,345,277, 5,785,841, and 4,365,027 reads were retrieved from each library (Table 1). The reads were considered to be sRNA-originated. Sequence length distribution analysis revealed that high-confidence unique reads spanned 18–30 nucleotides (nt), with 21–24 nt sequences representing the dominant fraction across all libraries. Notably, the 24 nt species exhibited peak abundance (Figure S1), consistent with plant RNA silencing pathways.

3.2. Identification of Known, Conserved, and Novel Peanut miRNAs

Peanut small RNAs were systematically classified through hierarchical annotation: (1) conserved miRNAs with a canonical hairpin structure in their precursor sequences, which were aligned with plant miRNAs in miRBase (http://www.mirbase.org/, accessed on 22 October 2023); (2) novel peanut miRNAs that had a hairpin structure of their precursor sequences but were not listed in the microRNA database; and (3) peanut siRNAs that did not have a hairpin structure in their precursor sequences. According to these criteria, we retrieved 1396, 1530, 1642, 1508, and 1542 unique conserved peanut miRNAs, respectively, and 1477, 1814, 1895, 1689, and 1667 novel peanut miRNAs in the C0, M12, M24, T12, and T24 libraries, respectively (Table 1 and Table S1).

3.3. Revealed Differential Expression Profiling of miRNAs Between PEG6000-Treated and Mock Control Groups

Differential miRNA expression profiling was performed using TPM-normalized read counts across five experimental conditions (C0, M12, M24, T12, and T24). We identified 42 and 72 differentially expressed miRNAs (DEMs) in the T12 vs. C0 and T24 vs. C0 comparisons, respectively. In contrast, 19 and 33 DEMs were observed in the M12 vs. C0 and M24 vs. C0 comparisons (Figure 2A). Among these, 10 and 19 miRNAs were upregulated in their expression and 9 and 14 miRNAs were downregulated at M12 and M24 compared to C0, respectively. Equally, 17 and 40 miRNAs were upregulated in their expression and 25 and 32 miRNAs showed downregulated expression at T12 and T24 compared to C0, respectively (Figure 2B). To delineate drought-responsive miRNAs, we performed direct comparisons between the treatment groups (T12/T24) and time-matched mock controls (M12/M24) by employing the following criteria: (1) total reads ≥ 200; (2) [treated/mock] ≥ 2 or [treated/mock] ≤ 0.5 in at least one stage. In the end, integrative analysis using the DESeq2 statistical method identified 10 conserved peanut miRNAs exhibiting significant differential expression (DE-miRNAs; Table 2, Tables S2 and S3).

3.4. Validation of Drought-Responsive miRNAs by Northern Blotting

Using reverse-cDNA fragments probes, we validated the bioinformatics predictions via Northern blotting. Since only single-nucleotide differences exist within (i) ahy-miR408 variants (ppt-miR408b, gma-miR408d, ath-miR408-3p, osa-miR408-3p) and (ii) ahy-miR398 variants (zma-miR398a-3p, ppe-miR398b, stu-miR398a-3p), reverse-complementary probes to these miRNAs can detect each entire miRNA family. Five Northern blots detected the candidate miRNAs. Notably, although the sequencing results revealed that ahy-miR395 was upregulated in both the mock and treatment groups, its expression at T24 showed a 21.96-fold increase compared to that at the baseline (Table 2 and Table S1). This suggests a drought stress response, leading to its inclusion in the subsequent analysis. ahy-miR398, ahy-miR408, and novel_14 showed significant downregulation in the PEG-treated vs. mock groups, indicating drought responsiveness (Figure 3A). novel_65 was undetectable (Figure 3B). Surprisingly, ahy-miR395 exhibited stable, high-level expression despite predicted differential patterns (Figure 3B). Subsequent work will focus on functional analysis of the conserved miRNAs ahy-miR398 and ahy-miR408.

3.5. Target Identification and Validation of Drought-Responsive miRNAs

To investigate the biological functions of drought-responsive miRNAs, we performed high-throughput degradome sequencing for genome-wide identification of miRNA targets. A degradome sequencing library (designated DS) was constructed using RNA pooled from five experimental cohorts: C0, M12, M24, T12, and T24. In total, we obtained 28,245,417 raw reads from the DS library. After filtering the reads without the 3′ adaptor sequence, 8,804,765 unique raw reads were retained. These unique reads were aligned with the peanut genome, yielding 5,924,034 mapped reads (67.28% alignment rate). These mapped reads annotated 64,908 genes (76.56% of total annotated genes) (Table S4). Using the CleaveLand pipeline [14], we identified miRNA cleavage targets by requiring an alignment score ≤ 7 for candidate host genes. Cleaved transcripts were classified into five categories (0–4) based on the abundance and positional confidence of the cleavage signatures. A total of 7402 targets were identified in the DS library, with 272, 70, 2088, 656, and 4316 assigned to categories 0, 1, 2, 3, and 4, respectively (Table S5). We selected genes with categories < 2 as candidate targets, as these categories represent the highest-confidence cleaved targets with the most abundant and precise cleavage signatures. The rationale for this threshold is that categories 0 and 1 indicate highly confident and reproducible cleavage events, minimizing the risk of false positives. Among the drought-responsive miRNAs, ahy-miR398 was identified as predominantly targeting CCSs (Arahy.1K1ZSN and Arahy.2GQ0W5) which encode copper chaperones for Cu/Zn-superoxide dismutase (Figure 4A,B; Table S6). Concurrently, ahy-miR408 regulates a multi-layered network targeting (i) LAC12 (Arahy.SXC6IH), a laccase 12 gene; (ii) COPAs (Arahy.39IB73 and Arahy.ALH963), P-type ATPase copper transporters; and (iii) PCL (Arahy.XVX6ST), blue copper protein-like genes. Although COPAs and PCL were classified as category 2, we still regarded them as bona fide targets because both have been previously identified as ahy-miR408 targets (Figure 4C–F; Table S6). Collectively, these results validate CCSs, LAC12, COPAs, and PCL as bona fide targets of their corresponding miRNAs (ahy-miR398 and ahy-miR408) through degradome sequencing. These miRNAs may be involved in the drought-responsive process in peanut by negatively regulating their target genes.

3.6. Overexpression of Ahy-miR398 and Ahy-miR408 Negatively Regulates Drought Tolerance

To elucidate the functional roles of ahy-miR398 and ahy-miR408 in drought response, we generated transgenic Arabidopsis lines overexpressing these miRNAs under the control of the 35S promoter. While these experiments provide valuable preliminary insights, it is important to note that Arabidopsis, as a model plant, differs from peanut in its miRNA processing and drought response pathways. Therefore, the results should be interpreted with caution, and further studies in peanut will be essential to confirm these findings in the native system.
Under non-stress conditions, the seed germination rates of WT, ahy-miR398-OE, and ahy-miR408-OE plants showed no significant differences by day 5. However, under mannitol-simulated drought stress, both transgenic lines exhibited substantially reduced germination rates compared to those for the WT (Figure 5A,B), demonstrating their negative regulatory function in germination. Consistent with this phenotype, ahy-miR398-OE and ahy-miR408-OE plants developed significantly shorter primary roots than those of the controls under drought stress (Figure 5C,D). Furthermore, after being subjected to 20% (w/v) PEG6000 for 15 days, both overexpression lines displayed markedly exacerbated growth impairments relative to the WT (Figure 5E,F). These collective observations confirm that ahy-miR398 and ahy-miR408 act as negative regulators of drought tolerance in this heterologous system. However, given the differences in miRNA processing between Arabidopsis and peanut, further validation in the native peanut system is required to confirm these results.

4. Discussion

Through functional characterization of drought-responsive miRNAs in peanut, this study provides the first integrative transcriptomic evidence in a tetraploid legume crop revealing the coexistence of conserved and peanut-specific regulatory mechanisms underlying drought adaptation. Unlike previous studies in model or diploid species, our analysis identifies novel copper-associated regulatory modules—ahy-miR398–CCS and ahy-miR408–LAC/COPA/PCL—that fine-tune ROS homeostasis through copper trafficking and utilization. These findings uncover a previously unrecognized link between miRNA-mediated copper metabolism and oxidative stress regulation in drought-tolerant peanut cultivars. Integrated transcriptome, small RNA, and degradome analyses further demonstrate that ahy-miR398 and ahy-miR408 act through distinct yet functionally convergent molecular pathways, both contributing to copper-dependent ROS detoxification. Collectively, this work not only deepens our understanding of miRNA-based oxidative stress regulation in polyploid crops but also establishes peanut-specific mechanistic insights that distinguish its drought response from those of other plant species.
miR398, a conserved miRNA in plants, regulates diverse stress responses and developmental processes across species. Initially identified in Arabidopsis and rice through base-pairing predictions [17], miR398 predominantly targets evolutionarily conserved genes such as Cu/Zn superoxide dismutase1 (CSD1) and CSD2, which are critical for ROS scavenging [18,19]. The miR398-CSD regulatory module governing oxidative stress management is conserved in seed plants. Studies in pea (Pisum sativum) and common bean (Pisum vulgaris) demonstrate reduced miR398 accumulation and concurrent upregulation of CSD1 under water deficit [20,21]. Similarly, in PEG-treated peanuts, ahy-miR398b shows downregulated expression and targets Cu/Zn-SOD to negatively modulate drought tolerance [22]. Rice overexpression experiments further confirm osa-miR398′s adverse role in drought adaptation, as transgenic lines exhibit heightened stress sensitivity [22]. Under drought stress, plants experience elevated levels of reactive oxygen species (ROS), particularly superoxide (O2•−), which causes cellular damage. Cu/Zn-superoxide dismutase (SOD1) is a crucial antioxidant enzyme that detoxifies O2•− into oxygen and hydrogen peroxide [17]. However, SOD1 requires the insertion of copper (Cu) into its active site to function. The Cu chaperone for SOD1 (CCS) specifically facilitates this essential post-translational activation. CCS binds intracellular copper ions and directly donates them to apo-SOD1, converting it into the active holo-enzyme. Crucially, CCS gene expression and protein levels often increase in response to drought and other stresses triggering ROS accumulation. Enhanced CCS activity ensures sufficient active SOD1 is available to efficiently scavenge cytotoxic superoxide radicals generated during water deficit. Plants overexpressing CCS generally demonstrate improved SOD activity and significantly enhanced tolerance to drought stress, while CCS mutants exhibit greater sensitivity. Thus, CCS-mediated activation of SOD1 is a vital component of the antioxidant defense system protecting cells against drought-induced oxidative damage [17]. Similarly to the above study, in peanuts, the activation of AhCCS genes (Arahy.1K1ZSN and Arahy.2GQ0W5) (Figure 4A,B) likely mitigates drought-induced oxidative damage by suppressing excessive ROS production, highlighting ahy-miR398′s negative regulatory function (Figure 5) through Cu/Zn-SOD-mediated oxidative stress pathways. miR408 is also an ancient and highly conserved miRNA involved in regulating plant growth, development, and stress responses [23]. Studies demonstrate that miR408 overexpression enhances drought tolerance through multi-target coordination. Under drought stress, the miR408 expression was downregulated in Prunus dulcis [24], Lycopersicon esculentum [25], Pisum sativum [20], A. thaliana [26], P. persica [24], and Ipomoea campanulata [27]. The response of miR408 to drought stress also depends on cultivar. When Convolvulaceae was exposed to drought stress, the expression of miR408 was downregulated in tolerant wild I. campanulata and upregulated in sensitive cultivated Jacquemontia pentantha [28]. Interestingly, consistent with these findings, this study observed downregulation of ahy-miR408 in the drought-tolerant peanut cultivar Fenhua 8 after PEG treatment, indicating that tolerant peanut cultivars suppress miR408 expression under drought (Table 2 and Figure 3). Functional analysis revealed that ahy-miR408 overexpression in Arabidopsis significantly reduced the seed germination rate, root length, and seedling growth compared to those in the controls, suggesting a negative regulatory role in drought tolerance (Figure 5). Thus, drought-tolerant peanuts likely resist drought by downregulating ahy-miR408. This aligns with previous studies: in Oryza sativa, miR408 overexpression decreased drought tolerance and the survival rate after recovery and increased water loss rates [29]. Advanced genomic technologies (e.g., next-generation sequencing) clarify that ahy-miR408 targets genes encoding copper (Cu) proteins [30], such as plantacyanin (PLC), uclacyanin (UCL), cupredoxin, laccase (LAC), and P-type ATPase copper transporters. These targets include proteins with plantacyanin/plastocyanin-like domains. By downregulating these genes, ahy-miR408 conserves Cu for plastocyanin, stabilizes plastocyanin levels, and increases reactive oxygen species (ROS), which promotes stomatal closure [23]. Additionally, the ahy-miR408 promoter is suppressed by ABA-responsive transcription factors, enabling plastocyanin (PCY) accumulation under stress. PCY elevates ROS in the guard cells, promotes stomatal closure, reduces photosynthetic gas exchange, and enhances drought resistance [23].This study further found that ahy-miR408 targets plastocyanin-like domain-containing genes: PCL (Arahy.XVX6ST), LAC12 (Arahy.SXC6IH), and COPAs (Arahy.39IB73 and Arahy.ALH963) (Figure 4). GO analysis confirmed their involvement in Cu binding and transport. Thus, ahy-miR408 likely regulates drought tolerance by modulating ROS levels through Cu transport (Table S6). While our heterologous Arabidopsis system provided initial functional validation (Figure 5), species-specific differences in miRNA processing (e.g., AGO protein affinity) [31] necessitate transgenic verification in peanut.
In summary, drought-tolerant peanut cultivars enhance their resistance under drought stress by downregulating ahy-miR398 and ahy-miR408. These miRNAs modulate drought tolerance through copper-mediated ROS management, thereby improving plant stress resistance (Figure 6). While the 20% PEG6000 treatment is effective for simulating osmotic stress, it may not fully replicate the gradual soil moisture depletion observed under field drought conditions. Future studies should combine controlled-environment phenotyping with field-based RNA sequencing to identify miRNA targets that could provide actionable insights for drought adaptation in peanut. Additionally, we suggest that future research should focus on the generation of transgenic peanut lines to verify these findings, providing a more accurate understanding of the roles of ahy-miR398 and ahy-miR408 in drought stress adaptation in the native peanut system.

5. Conclusions

This study delineates the dual regulatory axis of peanut drought adaptation through conserved and neo-functionalized miRNA pathways. This transcriptomic investigation establishes ahy-miR398 and ahy-miR408 as central regulators of peanut drought tolerance through copper-mediated ROS management. ahy-miR398 fine-tunes oxidative stress responses by suppressing CCS-dependent SOD activation, while ahy-miR408 coordinates systemic copper allocation via P-ATPase regulation. These findings suggest that ahy-miR398 and ahy-miR408 are crucial in regulating copper homeostasis and ROS detoxification, thereby enhancing drought resilience in peanuts. From a practical perspective, these results position epigenetic-modifier-targeting miRNAs, such as ahy-miR398 and ahy-miR408, as promising candidates for molecular breeding strategies, particularly for marker-assisted selection (MAS). By targeting miRNAs involved in copper homeostasis and oxidative stress management, we can develop climate-resilient peanut varieties tailored to arid regions where traditional breeding has been limited by environmental challenges. This approach could help overcome the limitations of conventional breeding, particularly in areas facing water scarcity and increasing drought stress. These miRNAs represent new opportunities for improving peanut productivity and stability, contributing to more sustainable agriculture in the context of climate change.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agriculture15212190/s1. Figure S1: Length distribution and abundance of the sequences; Table S1: List of identified miRNAs differentially expressed in peanut with drought treatment; Table S2: List of some candidate miRNAs involved in drought response in each library in repeat 2; Table S3: List of some candidate miRNAs involved in drought response in each library in repeat 3; Table S4: Statistics of degradome reads in DS; Table S5: Statistical overview of degradome-identified miRNA targets; Table S6: Target genes of ahy-miR398 and ahy-miR408; Table S7: Primer information.

Author Contributions

Conceptualization: D.B.; data curation: X.Z. (Xin Zhang); formal analysis: X.Z. (Xin Zhang); funding acquisition: D.B.; investigation: Y.T., Y.X., H.Z. and N.L.; methodology: R.Z. and Z.C.; project administration: D.B.; resources: D.B.; supervision: D.B.; validation: X.Z. (Xiaoyu Zhang); visualization: X.Z. (Xiaoji Zhang); writing—original draft: X.Z. (Xin Zhang); writing—review and editing: D.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Fundamental Research Program of Shanxi Province (No.: 202203021221178), the Science and Technology Major Project of Shanxi Province (No.: 202201140601025), the earmarked fund for Modern Agro-industry Technology Research System (No.: 2025CYJSTX05), and the National Peanut Industry Technology System Construction (No.: CARS-13).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

All data generated or analyzed in this study are included in this article and the supplemental files. The raw data from RNA sequencing were submitted to the NCBI database with the bioproject ID PRJNA1312004.The raw data from degradome sequencing were submitted to the NCBI database with the bioproject ID PRJNA1312356.

Acknowledgments

We acknowledge assistance with English language editing.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
TPMTranscripts per kilobase of exon model per million mapped reads
ROSReactive oxygen species
CCSCu chaperone for SOD
PLCPlantacyanin
PCLBlue copper protein-like
PCYPlastocyanin
UCLUclacyanin
LACLaccase
DEMDifferentially expressed miRNA
COPAP-type ATPase copper transporter
PCRPolymerase chain reaction
MSMurashige and Skoog

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Figure 1. Phenotypic response and chromosomal read distribution of peanut (A. hypogaea cv. Fenhua 8) to PEG6000-simulated drought stress. (A) Time-course phenotypic analysis at the four-leaf stage. Seedlings maintained at 27 °C with 70% relative humidity under 14 h light/10 h dark cycles show comparable morphology between mock and PEG6000-treated groups at C0 (initial), M12 (Mock-12 h), M24 (Mock-24 h), T12 (PEG6000-12 h), and T24 (PEG6000-24 h). No significant morphological alterations were observed in PEG6000-treated seedlings relative to mock controls after 12 h or 24 h treatments. Time-series images composited using Adobe Photoshop CS6. (B) Read density distribution across chromosomes. Circos visualization displays read distribution per chromosome. The outermost ring shows selected chromosomes of the peanut Tifrunner reference genome (assembly gnm2). For example, arahy.tifrunner.gnm2.arahy.14 refers to chromosome 14 of the A. hypogaea Tifrunner genome. The middle gray zone represents a subset of 10,000 reads (red = positive strand, blue = negative strand). The innermost ring plots all chromosome-aligned reads, with orange indicating positive-strand coverage distribution and green denoting negative-strand coverage distribution, where outliers exceeding the mean + 3 standard deviations of all coverage data are excluded.
Figure 1. Phenotypic response and chromosomal read distribution of peanut (A. hypogaea cv. Fenhua 8) to PEG6000-simulated drought stress. (A) Time-course phenotypic analysis at the four-leaf stage. Seedlings maintained at 27 °C with 70% relative humidity under 14 h light/10 h dark cycles show comparable morphology between mock and PEG6000-treated groups at C0 (initial), M12 (Mock-12 h), M24 (Mock-24 h), T12 (PEG6000-12 h), and T24 (PEG6000-24 h). No significant morphological alterations were observed in PEG6000-treated seedlings relative to mock controls after 12 h or 24 h treatments. Time-series images composited using Adobe Photoshop CS6. (B) Read density distribution across chromosomes. Circos visualization displays read distribution per chromosome. The outermost ring shows selected chromosomes of the peanut Tifrunner reference genome (assembly gnm2). For example, arahy.tifrunner.gnm2.arahy.14 refers to chromosome 14 of the A. hypogaea Tifrunner genome. The middle gray zone represents a subset of 10,000 reads (red = positive strand, blue = negative strand). The innermost ring plots all chromosome-aligned reads, with orange indicating positive-strand coverage distribution and green denoting negative-strand coverage distribution, where outliers exceeding the mean + 3 standard deviations of all coverage data are excluded.
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Figure 2. Differentially expressed miRNAs in mock and PEG6000 treatments. (A) Venn diagrams showing the number of common and specific miRNAs in comparisons of the five libraries. (B) Volcanic diagrams showing the number of differentially expressed miRNAs in each comparison. The red dots indicate miRNAs with significant differences, and the blue dots indicate that the difference was not significant for miRNA expression.
Figure 2. Differentially expressed miRNAs in mock and PEG6000 treatments. (A) Venn diagrams showing the number of common and specific miRNAs in comparisons of the five libraries. (B) Volcanic diagrams showing the number of differentially expressed miRNAs in each comparison. The red dots indicate miRNAs with significant differences, and the blue dots indicate that the difference was not significant for miRNA expression.
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Figure 3. Expression patterns of miRNAs under drought stress. (A) Northern blot analysis of ahy-miR398, novel_14, and ahy-miR408 in peanut (A. hypogaea L. cv. Fenhua 8) under PEG6000-induced drought stress. Samples were collected at 0 h (baseline, C0), 12 h mock (M12), 24 h mock (M24), 12 h PEG6000 treatment (T12), and 24 h PEG6000 treatment (T24). (B) Northern blot analysis of ahy-miR395 and novel_65 under the same conditions. Each blot was hybridized with a DNA oligonucleotide probe complementary to the indicated miRNA; probes could detect all family members differing by a single nucleotide. U6 was used as a loading control. 100 µg of total RNA was loaded per lane. Numbers below each blot represent the relative abundance (RA) of each miRNA normalized to U6. Under PEG6000 treatment, ahy-miR398 and ahy-miR408 showed marked downregulation, and novel_65 was undetectable, while ahy-miR395 maintained high and relatively stable expression levels.
Figure 3. Expression patterns of miRNAs under drought stress. (A) Northern blot analysis of ahy-miR398, novel_14, and ahy-miR408 in peanut (A. hypogaea L. cv. Fenhua 8) under PEG6000-induced drought stress. Samples were collected at 0 h (baseline, C0), 12 h mock (M12), 24 h mock (M24), 12 h PEG6000 treatment (T12), and 24 h PEG6000 treatment (T24). (B) Northern blot analysis of ahy-miR395 and novel_65 under the same conditions. Each blot was hybridized with a DNA oligonucleotide probe complementary to the indicated miRNA; probes could detect all family members differing by a single nucleotide. U6 was used as a loading control. 100 µg of total RNA was loaded per lane. Numbers below each blot represent the relative abundance (RA) of each miRNA normalized to U6. Under PEG6000 treatment, ahy-miR398 and ahy-miR408 showed marked downregulation, and novel_65 was undetectable, while ahy-miR395 maintained high and relatively stable expression levels.
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Figure 4. T-plot analysis of miRNA target genes validated through degradome sequencing. Each T-plot shows the distribution of degradome tags across the full length of the target mRNA. The vertical line jointly indicated by the red circle and the arrow represents the precise cleavage site of the transcript. The corresponding miRNA–mRNA pairing is shown below each plot. Statistical significance (p-value) and category classification are indicated above each panel. (A,B) Cleavage of CCSs (Arahy.1K1ZSN) and Arahy.2GQ0W5 mRNA mediated by ahy-miR398. (C–F) Cleavage of LAC12 (Arahy.SXC6IH), COPAs (Arahy.39IB73, Arahy.ALH963), and PCL (Arahy.XVX6ST) mRNA mediated by ahy-miR408.
Figure 4. T-plot analysis of miRNA target genes validated through degradome sequencing. Each T-plot shows the distribution of degradome tags across the full length of the target mRNA. The vertical line jointly indicated by the red circle and the arrow represents the precise cleavage site of the transcript. The corresponding miRNA–mRNA pairing is shown below each plot. Statistical significance (p-value) and category classification are indicated above each panel. (A,B) Cleavage of CCSs (Arahy.1K1ZSN) and Arahy.2GQ0W5 mRNA mediated by ahy-miR398. (C–F) Cleavage of LAC12 (Arahy.SXC6IH), COPAs (Arahy.39IB73, Arahy.ALH963), and PCL (Arahy.XVX6ST) mRNA mediated by ahy-miR408.
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Figure 5. Overexpression of ahy-miR398 and ahy-miR408 compromises drought tolerance across Arabidopsis development. (A,B) Seed germination sensitivity of ahy-miR398-overexpressing (ahy-miR398-OE) and ahy-miR408-overexpressing (ahy-miR408-OE) lines under drought stress. Wild-type (WT), ahy-miR398-OE, and ahy-miR408-OE Arabidopsis seeds were plated onto 1/2 MS media supplemented with 300 mM mannitol for drought simulation or mock control. After 5 days of treatment, both transgenic lines exhibited significantly reduced germination rates compared to those for the WT under mannitol-induced drought stress (** p < 0.01) while showing comparable germination under non-stress conditions. Quantification of the seed germination rates is shown in Panel B. (C,D) Root development in miRNA-overexpressing lines during drought stress. Primary root lengths of WT, ahy-miR398-OE, and ahy-miR408-OE seedlings were analyzed after 7 days of growth on 1/2 MS media with 150 mM mannitol. Representative images (left panel) show root elongation under drought conditions. The quantification of root length is shown in Panel D (** p < 0.01). (E,F) Growth suppression under progressive drought stress. Two-week-old WT and transgenic plants were subjected to 20% (w/v) PEG6000 for 15 days to simulate drought treatment. Representative images (left) show visible wilting/yellowing and stunted growth in transgenic plants compared to the WT. The quantification of fresh weight after 15 days of PEG6000 treatment is shown in Panel F (** p < 0.01).
Figure 5. Overexpression of ahy-miR398 and ahy-miR408 compromises drought tolerance across Arabidopsis development. (A,B) Seed germination sensitivity of ahy-miR398-overexpressing (ahy-miR398-OE) and ahy-miR408-overexpressing (ahy-miR408-OE) lines under drought stress. Wild-type (WT), ahy-miR398-OE, and ahy-miR408-OE Arabidopsis seeds were plated onto 1/2 MS media supplemented with 300 mM mannitol for drought simulation or mock control. After 5 days of treatment, both transgenic lines exhibited significantly reduced germination rates compared to those for the WT under mannitol-induced drought stress (** p < 0.01) while showing comparable germination under non-stress conditions. Quantification of the seed germination rates is shown in Panel B. (C,D) Root development in miRNA-overexpressing lines during drought stress. Primary root lengths of WT, ahy-miR398-OE, and ahy-miR408-OE seedlings were analyzed after 7 days of growth on 1/2 MS media with 150 mM mannitol. Representative images (left panel) show root elongation under drought conditions. The quantification of root length is shown in Panel D (** p < 0.01). (E,F) Growth suppression under progressive drought stress. Two-week-old WT and transgenic plants were subjected to 20% (w/v) PEG6000 for 15 days to simulate drought treatment. Representative images (left) show visible wilting/yellowing and stunted growth in transgenic plants compared to the WT. The quantification of fresh weight after 15 days of PEG6000 treatment is shown in Panel F (** p < 0.01).
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Figure 6. The proposed regulatory model of ahy-miR398 and ahy-miR408 in peanut drought tolerance. Under normal conditions (left panel), ahy-miR398 and ahy-miR408 are highly expressed, leading to suppression of their target genes, including CCSs (the Cu chaperone for SOD), PCL (blue copper protein-like), COPAs (P-type ATPase copper transporters), and LAC12 (laccase 12). This maintains basal oxidative homeostasis and basal tolerance. Under drought stress (right panel), both ahy-miR398 and ahy-miR408 are downregulated, releasing inhibition of their target genes. The increased expression of CCSs, PCL, COPAs, and LAC12 enhances copper homeostasis and superoxide dismutase activation, thereby promoting reactive oxygen species (ROS) scavenging and improving drought tolerance. Overall, downregulation of ahy-miR398 and ahy-miR408 under drought conditions contributes to enhanced antioxidative capacity and stress resilience in peanut.
Figure 6. The proposed regulatory model of ahy-miR398 and ahy-miR408 in peanut drought tolerance. Under normal conditions (left panel), ahy-miR398 and ahy-miR408 are highly expressed, leading to suppression of their target genes, including CCSs (the Cu chaperone for SOD), PCL (blue copper protein-like), COPAs (P-type ATPase copper transporters), and LAC12 (laccase 12). This maintains basal oxidative homeostasis and basal tolerance. Under drought stress (right panel), both ahy-miR398 and ahy-miR408 are downregulated, releasing inhibition of their target genes. The increased expression of CCSs, PCL, COPAs, and LAC12 enhances copper homeostasis and superoxide dismutase activation, thereby promoting reactive oxygen species (ROS) scavenging and improving drought tolerance. Overall, downregulation of ahy-miR398 and ahy-miR408 under drought conditions contributes to enhanced antioxidative capacity and stress resilience in peanut.
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Table 1. Distribution of sRNAs among different categories in each library.
Table 1. Distribution of sRNAs among different categories in each library.
TypesNormalized Reads
MockPEG6000
C0 (0 h)M12 (12 h)M24 (24 h)T12 (12 h)T24 (24 h)
Total sRNA8,318,20912,456,06514,081,18511,767,47614,303,309
Mapped sRNA8,216,52712,298,86713,877,51211,602,16814,101,597
  unique reads1,800,2332,923,6982,787,5192,625,3053,199,519
Rfam2,971,4904,180,1764,808,3524,528,6655,440,521
  rRNA2,850,7334,006,3164,612,9134,330,6445,219,816
  tRNA105,892151,185173,190177,060196,697
  snRNA795130614169211401
  snoRNA14,07021,36920,83320,04022,607
Repeat910,9211,300,3541,723,8831,287,6621,349,558
Peanut small RNA4,334,1166,818,3377,345,2775,785,8414,365,027
  Conserved_miRNA (Total)701,945787,6911,068,334589,234643,141
  Conserved_miRNA (Unique)13961530164215081542
  Novel_miRNA (Total)65,97075,236100,87956,35163,170
  Novel_miRNA (Unique)14771814189516891667
  TAS (Total)21702775304119292045
  TAS (Unique)257342329288302
  NAT (Total)704,7411,025,4611,204,380936,8251,145,790
  NAT (Unique)163,853239,479239,611221,336249,808
  exon:+188,098261,121327,090222,60473,051
  exon:−114,204168,497195,878134,02640,458
  intron:+174,486301,486296,639258,942117,667
  intron:−107,808190,269182,374167,24689,658
  other2,274,6944,005,8013,966,6623,418,6842,190,047
A peanut sRNA deep sequencing summary is presented. The abundance in different libraries was normalized to that in the library at C0, and sRNAs were calculated as reads per million.
Table 2. List of some candidate miRNAs involved in drought response in each library.
Table 2. List of some candidate miRNAs involved in drought response in each library.
miRNA IdentifierNormalized Reads (reads/mgs)Fold-Change
MockPEG6000MockPEG6000
C0M12M24T12T24M12M24T12T24
ahy-miR408ppt-miR408b4641.2111,839.828686.842212.681375.162.551.870.480.30
gma-miR408d4634.5011,837.448682.562210.281373.682.551.870.480.30
ath-miR408-3p4633.1611,837.448682.562210.281373.682.551.870.480.30
osa-miR408-3p1467.483723.152688.65681.56523.452.541.830.460.36
ahy-novel_14novel_14663.991271.90794.63153.59248.421.921.200.230.37
ahy-novel_65novel_65268.28473.40534.03191.99102.031.761.990.720.38
ahy-miR395aath-miR395a52.3167.6371.20167.991148.931.291.363.2121.96
ahy-miR398zma-miR398a-3p104.63215.94166.6252.8016.272.061.590.500.16
ppe-miR398b79.14169.6798.2628.8029.572.141.240.360.37
stu-miR398a-3p57.68129.3389.7224.0011.832.241.560.420.21
Expression variations were analyzed. Peanut miRNAs with opposite changes in mock and PEG6000 treatment were selected by employing the following criteria: (1) total reads ≥ 200; (2) [treated/mock] ≥ 2 or [treated/mock] ≤ 0.5 in at least one stage.
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Zhang, X.; Zhang, R.; Chen, Z.; Zhang, X.; Zhang, X.; Tian, Y.; Xue, Y.; Zhang, H.; Li, N.; Bai, D. Characterization of Drought-Responsive miRNAs in Peanut Through Integrated Transcriptomic Approaches. Agriculture 2025, 15, 2190. https://doi.org/10.3390/agriculture15212190

AMA Style

Zhang X, Zhang R, Chen Z, Zhang X, Zhang X, Tian Y, Xue Y, Zhang H, Li N, Bai D. Characterization of Drought-Responsive miRNAs in Peanut Through Integrated Transcriptomic Approaches. Agriculture. 2025; 15(21):2190. https://doi.org/10.3390/agriculture15212190

Chicago/Turabian Style

Zhang, Xin, Rui Zhang, Zhenbo Chen, Xiaoyu Zhang, Xiaoji Zhang, Yuexia Tian, Yunyun Xue, Huiqi Zhang, Na Li, and Dongmei Bai. 2025. "Characterization of Drought-Responsive miRNAs in Peanut Through Integrated Transcriptomic Approaches" Agriculture 15, no. 21: 2190. https://doi.org/10.3390/agriculture15212190

APA Style

Zhang, X., Zhang, R., Chen, Z., Zhang, X., Zhang, X., Tian, Y., Xue, Y., Zhang, H., Li, N., & Bai, D. (2025). Characterization of Drought-Responsive miRNAs in Peanut Through Integrated Transcriptomic Approaches. Agriculture, 15(21), 2190. https://doi.org/10.3390/agriculture15212190

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