Identification of miRNAs and Profiling of ROS Metabolism in Response to Saline–Alkali Stress in Wheat (Triticum aestivum L.)

Abstract

Saline–alkali stress is one of the important abiotic stresses, which affect plant growth and development. However, the understanding of miRNA pathways in different saline–alkali stress is still limited. In order to better understand the salt–alkali stress response mechanism of wheat, we analyzed miRNA transcription levels in two wheat varieties differing in saline–alkali tolerance (Qingmai 6, QM, tolerant; Meisheng 0308, MS, sensitive) under mixed saline–alkali stress (150 mmol·L¹ and 300 mmol·L¹) for 7 days. High-throughput sequencing identified 11,368 miRNAs (106 conserved, 11,262 non-conserved), among which four miRNAs (miR9653b, miR5384-3p, miR9777, and miR531) exhibited a consistent expression trend across both varieties and all stress concentrations. Additionally, a potential miRNA-mediated regulatory network (including miR408 and miR1135) was predicted to regulate reactive oxygen species (ROS) metabolism via cytochrome P450, plant hormone signal transduction, and MAPK pathways. Saline–alkali-tolerant and sensitive wheat cultivars exhibited distinct miRNA expression patterns under stress. QM maintained higher contents of non-enzymatic antioxidants (ascorbic acid, AsA; reduced glutathione, GSH) and activities of key antioxidant enzymes (ascorbate peroxidase, APX; glutathione reductase, GR), which contributed to balanced ROS homeostasis and enhanced saline–alkali tolerance.

1. Introduction

Wheat, the third largest grain crop of China, is cultivated worldwide as a cereal crop. As one of the most important rations in various countries and regions worldwide [1], wheat is also the most important ration of Northern China. Therefore, the importance of its stable development is evident. With the global population increase and the natural environment’s deterioration, soil salinisation has become an increasingly serious global problem [2]. Approximately 7% of the world’s land area (more than 900 million hectares) is threatened by climate change. The area of saline–alkali land in China has reached 100 million hectares, including 3.73 million hectares in the Songnen Plain in Northeast China [3], which is one of the three typical saline–alkali lands in the global soil distribution area. Therefore, soil salinisation is a widespread source of abiotic stress and has become a major limiting factor in global crop production.

After long-term saline–alkali stress, plants change their morphology to better adapt to the environment [4]. Leaf anatomical structure analysis revealed that salt-tolerant Atriplex glauca L. has thicker leaves than other plants, due to increased epidermal and mesophyll thickness and enhanced mesophyll density [5]. Under saline–alkali stress, the accumulation of sodium ions in the soil solution leads to higher osmotic pressure of the soil solution than that of the plant cell sap, and water flows out of plant cells, leading to osmotic stress and physiological drought [6]. Plant cells synthesise and accumulate several small-molecule organic compounds, such as proline, soluble proteins, betaine, sugar, polyols, and polyamines, to maintain their water potential in cells and cope with this stress [7]. Under saline–alkali stress, high concentrations of sodium ions in the soil destroy the dynamic balance of ions in cells, leading to a series of destructive effects on plants, such as the destruction of cell membrane structure, abnormal metabolism in cells, and ion toxicity [8]. Saline–alkali stress promotes the formation and accumulation of reactive oxygen species (ROS), which affects the physiological function of cells and leads to metabolic disorders. Eliminating the effects of ROS metabolism is of great significance in alleviating damage to wheat seedlings under saline–alkali stress.

MiRNAs are a class of non-coding RNAs approximately 20–24 nucleotides (nt) in length. In plants, the abundance and diversity of miRNAs enable them to readily regulate most biological processes in the organism via one or more specific miRNAs [9]. MiRNAs play regulatory roles in key plant physiological processes, such as plant growth and development, as well as responses to biotic and abiotic environmental stresses [10]. It has been reported that miRNAs are differently regulated in different plant species, such as wheat [11], corn [3], soybean [12], and Arabidopsis [13], under saline–alkali stress. Specific post-transcriptional regulation mediated by miRNAs is critical for improving crop resistance to abiotic stresses [14]. Previous studies have confirmed that, under high salt stress, specific wheat miRNAs enhance salt tolerance by modulating the expression levels of genes related to antioxidation, nutrient absorption, and lipid metabolic balance [15]. The molecular mechanism of salt tolerance in the roots of salt-tolerant wheat may involve the regulation of expression of miRNAs that target auxin response factors, which are involved in cell growth, ion homeostasis, and hormone signal transduction, thereby contributing to the enhancement of salt tolerance [16]. However, there are a few studies on the dynamic expression profiles of miRNAs in wheat varieties with contrasting saline–alkali tolerance under different concentrations of saline–alkali stress.

In this study, we aimed to characterise the expression patterns of saline-alkali stress-responsive wheat miRNAs via high-throughput sequencing and further determined the differentially expressed miRNAs (DEMs) in response to saline–alkali stress in wheat roots. Subsequently, the predicted target genes of these DEMs were functionally annotated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to clarify their functional roles in the plant’s saline–alkali stress response. Additionally, we investigated the regulatory mechanisms underlying reactive oxygen species (ROS) metabolism in saline–alkali-tolerant and -sensitive wheat cultivars under saline–alkali stress. Collectively, this study provides a systematic and comprehensive analysis of DEMs in wheat roots in response to saline–alkali stress and offers valuable insights into the differences in saline–alkali tolerance among wheat varieties, thereby aiding in elucidating the molecular mechanisms governing wheat seedling roots’ response to saline–alkali stress.

2. Materials and Methods

2.1. Biological Material

The experimental materials were two wheat (Triticum aestivum L.) varieties: Qingmai 6 (abbreviated as QM, saline–alkali-tolerant genotype, donated by Shandong Academy of Agricultural Sciences, Jinan, China) [17] and Meisheng 0308 (abbreviated as MS, saline–alkali-sensitive genotype, provided by professor Fu Zhaolin from our laboratory).

2.2. Cultivation of Wheat Under Laboratory Conditions

After the surface of the seeds were disinfected for 5 min (0.1% mercuric chloride), the residual mercuric chloride was washed with distilled water, and 50 plump, uniform caryopses were used per experimental variant. The caryopses were first sown in sterile Petri dishes with moist filter paper for 2 days of germination, then transplanted into plastic pots (10 cm × 10 cm × 12 cm) containing 1/5 Hoagland nutrient solution for 7 days. Cultivation was conducted in an intelligent artificial climate chamber (Model: RXZ-500D, Ningbo Jiangnan Instrument Factory, Ningbo, China) with the specified 12 h light/12 h dark cycle and 23 ± 2 °C temperature. For stress treatment, seedlings were exposed to 1/5 strength Hoagland nutrient solution supplemented with a saline–alkali mixture (Na₂SO₄:NaCl:Na₂CO₃:NaHCO₃ = 9:3:3:1, molar ratio) at two total salt concentrations: 150 mmol·L⁻¹ and 300 mmol·L⁻¹. Meanwhile, the control group was treated with 1/5 strength Hoagland nutrient solution (without additional saline–alkali).

Biological replicates were defined as independent experimental units; each replicate consisted of 50 seedlings grown in separate pots under identical environmental conditions (same climate chamber, nutrient solution, and stress treatment). Six experimental variants were established in this study, following the order: QMCK (control group for saline–alkali-tolerant wheat variety Qingmai 6), QM150 (Qingmai 6 treated with 150 mmol·L⁻¹ saline–alkali mixture), QM300 (Qingmai 6 treated with 300 mmol·L⁻¹ saline–alkali mixture), MSCK (control group for saline–alkali-sensitive wheat variety Meisheng 0308), MS150 (Meisheng 0308 treated with 150 mmol·L⁻¹ saline–alkali mixture), and MS300 (Meisheng 0308 treated with 300 mmol·L⁻¹ saline–alkali mixture).

Three biological replicates were established for each experimental variant (QMCK, QM150, QM300, MSCK, MS150, and MS300) and used for all analyses, including miRNA library sequencing, physiological index determination, and phenotypic trait measurement. After 7 days of growth, seedlings were collected, and roots were isolated from these seedlings for the measurement of physiological indicators, phenotypic analysis, and miRNA sequencing, except for the morphological indicators of leaves. The length of the leaves was measured as the straight-line distance from the ligule (the membranous structure at the junction of the leaf base and the leaf sheath) to the leaf tip. Average root length was determined by randomly selecting 10 plants per replicate and measuring the longest root of each selected plant with a vernier caliper (accuracy: 0.1 mm). Average root dry weight was determined by randomly selecting 10 plants per replicate and measuring all roots of each selected plant. All roots were blotted dry with absorbent paper, then oven-dried at 80 °C to a constant weight, and the dry weight was measured using an electronic balance (accuracy: 0.001 g).

2.3. Construction and Sequencing of miRNA Library

First, total RNA was extracted from the roots using the TRIzol (Invitrogen, CA, USA) method. The total RNA extracted was detected using Nanodrop, Qubit 2.0 (Thermo Fisher Scientific, CA, USA), and Agilent 2100 Bioanalyzer (Agilent Technologies, USA) methods to detect the purity, concentration, and integrity of RNA samples to ensure that qualified samples were used for sequencing. The samples were submitted to Nanjing Jisi Huiyuan Biotechnology Co., Ltd. (Nanjing, China). for testing. After the sample passed the quality control test, total RNA was used as the starting sample, and a small RNA sample prep kit (TIANGEN Biotech, Beijing, China)was used to construct the library. Since the small RNA has a phosphate group at the 5’ end and a hydroxyl group at the 3’ end, T4 RNA Ligase 1 and T4 RNA Ligase 2 (truncated) were used to add connectors at the 5’ and 3’ ends of the small RNA, respectively. Reverse transcription was used to synthesise cDNA and PCR amplification was performed. Target fragments were screened using PAGE analysis, and the fragments recovered by gel cutting were used as small RNA libraries. After the construction of the library, a Qubit 2.0 was used to determine the concentration of the library, an Agilent 2100 Bioanalyzer, and qRT-PCR were used to determine the insert size and effective concentration of the library, respectively, to ensure its quality. After library inspection, the NovaSeq 6000 platform (Illumina, Inc., San Diego, CA, USA) was used for high-throughput sequencing with a single-end (SE) reading length of 50 bp (base pairs).

2.4. Prediction and Analysis of miRNA

The unclassified ncRNA sequences were aligned to the reference sequence (depth ≥ 2) using Bowtie (v1.2.2) [18], generating mapped sequences with positional annotations. The reads corresponding to the reference genome (IWGSC RefSeq v2.1) were compared with miRBase to obtain annotation information for known miRNAs. The Rfam database was used to annotate the reads that did not conform to known miRNAs, filter ribosomal RNA (rRNA), transfer RNA (tRNA), intranuclear small RNA (snRNA), nucleolar small RNA (snoRNA), and other ncRNAs (non-coding RNAs) and repetitive sequences and obtained uncommented reads containing potential miRNAs. The sRNAs of Rfam and miRBase was then compared to the reference genome, the surrounding sequences intercepted, and miRDeep2 software (Version 2.0.1.2) was used to predict the secondary structure [19]. Based on the predicted results, they were filtered using Dicer digestion site information, energy values, and other characteristics to identify new miRNAs. The miRNA expression was standardised to tags per million (TPM), and differentially expressed miRNAs were detected using DESeq2 software (v1.38.0, Bioconductor, https://bioconductor.org/packages/DESeq2/, accessed on 25 January 2026) [20] with the following criteria: log₂FC (log₂ Fold Change) ≥ 1 (significantly upregulated) or log₂FC ≤ −1 (significantly downregulated), and FDR (False Discovery Rate) < 0.05. FDR is a statistical measure to control the proportion of false positives among significant results, while log₂FC represents the log₂-transformed ratio of miRNA expression levels between stress and control groups.

2.5. Determination of Plant Physiological Indexes

The superoxide radical (O₂⁻) content was determined by the hydroxylamine oxidation method [21]. The hydrogen peroxide (H₂O₂) content was measured using the titanium chloride method [22]. Malondialdehyde (MDA) content was assessed using the method described and improved [23]. Ascorbic acid (AsA) content was assayed according to the described method [24]. Superoxide dismutase (SOD) activity was measured based on nitrate blue tetrazolium (NBT) photoreduction [25]. The peroxidase (POD) activity was determined using guaiacol as the substrate [26]. Catalase (CAT) activity was mainly measured using the spectrophotometric assays [27] and ascorbate peroxidase (APX) activity was mainly assayed using the UV absorption method [28]. Glutathione reductase (GR) activity was determined according to a modified version of the method described [29]. Dehydroascorbate (DHA) content was determined by the method [30]. Oxidized glutathione (GSSG) and reduced glutathione (GSH) were estimated following the method [31]. Root activity was determined using a modified triphenyltetrazolium chloride (TTC) reduction method [32]. Briefly, fresh root samples (0.5 g) were incubated in TTC solution at 37 °C for 2 h, and the formazan produced was extracted with ethanol. The absorbance was measured at 485 nm, and root activity was expressed as μg formazan g⁻¹ fresh weight h⁻¹.

2.6. Statistical Analysis

All experimental data were analyzed using SPSS 26.0 software (IBM Corporation, Chicago, IL, USA). Significant differences between groups were determined by one-way analysis of variance (ANOVA) followed by Duncan’s multiple range test (p < 0.05). Error bars in figures and tables represent the standard deviation (SD) of three biological replicates.

3. Results

3.1. Determination of Salt and Alkali Resistance in Wheat Varieties QM and MS

The stem and leaf length, stem and leaf dry weight, root length, and root dry weight of the two wheat varieties (QM and MS) showed a decreasing trend with increasing saline–alkali concentration (Tables S1 and S2). Specifically, all growth indicators of the two varieties under 150 mmol·L⁻¹ and 300 mmol·L⁻¹ stress were significantly lower than those of the control group (CK). Notably, QM exhibited a lower magnitude of decrease in these growth indicators compared to MS, which directly reflects the stronger saline–alkali tolerance in QM.

3.2. Identification of miRNA

Quality control of the original sequencing data showed that the clean reads of all samples were greater than 10.90 M and the Q30 value rate exceeded 96% (

Table 1. Related quality metrics of samples after quality control of original sequencing data.

Sample	Clean Reads	Q30 (%)	Useful Reads
MSCK	13,385,632	96.62	9,857,737
MS150	11,994,266	96.21	9,477,373
MS300	20,926,054	96.17	15,433,224
QMCK	11,385,528	96.22	7,296,410
QM150	11,547,046	96.21	8,234,871
QM300	10,901,483	96.15	7,830,689

), indicating stable and reliable data that met the requirements of subsequent analysis. The original validated data (useful reads) of 18–30 nt (nucleotides) in length that was preliminarily screened could also be directly used for subsequent analysis.

A total of 11,368 miRNAs were detected within the six samples, including 106 conserved miRNAs and 11,262 non-conserved miRNAs. Consistent with the characteristics of Dicer and Dicer-like (DCL) enzyme cleavage, mature miRNAs were mainly distributed in the 20–24 nt length range, with 21 nt miRNAs being the most abundant, followed by the 22 nt miRNAs (Figure 1).

When Dicer and DCL enzymes recognise and cleave precursor miRNAs, the first base pair at the 5’ end has another high and strong bias toward U. By analysing the base preference in miRNA, a typical base proportion in miRNA was obtained (Figure 2). The proportion of base U at Site 1 was approximately 60%, which was 25% higher than the natural proportion. At the same time, it was also significantly greater than the average proportion of the first three sites of the other sites, indicating a high degree of genetic preference. The average proportion of base U at loci 21 and 22 was approximately 20%, which was less than 25% of the proportion of randomly distributed bases at the other four loci, reflecting a certain genetic preference. The data from these experiments also show that the miRNA sequencing and identification results maintained a relatively high level of stability.

3.3. Differential miRNA Expression Analysis

MiRNAs with significant differential expressions in the sample sequence were screened. There were 15 differential miRNAs (11 significantly upregulated and 4 significantly downregulated) under MS low salt–alkali stress (MS150) (

Table 2. Quantitative information of 12 differentially expressed miRNAs in MSCK vs. MS150.

miRNA	FDR	log2FC	Regulated
tae-miR9653b	3.72 × 10−18	5.720640275	up
tae-miR5384-3p	3.43 × 10−10	5.587716988	up
tae-miR9663-5p	6.63 × 10−5	−10.53414846	down
tae-miR9777	6.63 × 10−5	3.36869441	up
tae-miR159a	0.004637655	1.678595608	up
tae-miR9657b-3p	0.004637655	5.841963605	up
tae-miR159b	0.004637655	1.666456247	up
tae-miR531	0.007242266	1.779224377	up
tae-miR1127b-3p	0.019185208	−9.174834348	down
tae-miR167a	0.019185208	1.49161006	up
tae-miR160	0.026106175	1.405935876	up
tae-miR9676-5p	0.028654263	1.620398559	up

Note: The standard definition of screening method for homology differentially expressed genes. FDR = False Discovery Rate (controls false positive proportion); log₂FC = log₂ fold-change (log₂-transformed ratio of expression in stress group vs. control group); log₂FC ≥ 1 (upregulated) or log₂FC ≤ −1 (downregulated) with FDR < 0.05 were considered significantly differentially expressed.

), 12 (10 significantly upregulated and 2 significantly downregulated) under MS hypersaline stress (MS300) (

Table 3. Quantitative information of 15 differentially expressed miRNAs in MSCK vs. MS300.

miRNA	FDR	log2FC	Regulated
tae-miR9653b	1.16 × 10−17	5.634395165	up
tae-miR171b	1.84 × 10−8	4.309255096	up
tae-miR167a	0.000106897	2.265212923	up
tae-miR1121	0.000111422	3.356696522	up
tae-miR5384-3p	0.000111422	4.180628237	up
tae-miR531	0.000111422	2.275712163	up
tae-miR159a	0.00012306	2.088700948	up
tae-miR159b	0.00012306	2.084338809	up
tae-miR160	0.000439108	1.946799708	up
tae-miR9657a-3p	0.000439108	−9.730351265	down
tae-miR9664-3p	0.000601365	−3.399305364	down
tae-miR9777	0.005659187	2.491608318	up
tae-miR9674a-5p	0.005659187	−1.842074424	down
tae-miR9779	0.038061412	1.733672012	up
tae-miR156	0.038061412	−1.304491268	down

Note: The standard definition of screening method for homology differentially expressed genes. FDR = False Discovery Rate (controls false positive proportion); log₂FC = log₂ fold-change (log₂-transformed ratio of expression in stress group vs. control group); log₂FC ≥ 1 (upregulated) or log₂FC ≤ −1 (downregulated) with FDR < 0.05 were considered significantly differentially expressed.

), 12 (6 significantly upregulated and 6 significantly downregulated) under QM low salt–alkali stress (QM150) (

Table 4. Quantitative information of 12 differentially expressed miRNAs in QMCK vs. QM150.

miRNA	FDR	log2FC	Regulated
tae-miR9653b	7.54 × 10−23	6.873134298	up
tae-miR9777	4.90 × 10−15	6.283186624	up
tae-miR7757-5p	3.95 × 10−9	3.225551883	up
tae-miR9779	2.01 × 10−5	3.27327285	up
tae-miR5384-3p	0.000105389	4.484857755	up
tae-miR9662b-3p	0.000111358	−2.165545203	down
tae-miR9662a-3p	0.000111358	−2.159280859	down
tae-miR531	0.000114928	2.50742233	up
tae-miR408	0.005486524	−2.073549655	down
tae-miR171a	0.005486524	−1.759242957	down
tae-miR1125	0.030812121	−8.840774091	down
tae-miR164	0.042120985	−1.346351549	down

Note: The standard definition of screening method for homology differentially expressed genes. FDR = False Discovery Rate (controls false positive proportion); log₂FC = log₂ fold-change (log₂-transformed ratio of expression in stress group vs. control group); log₂FC ≥ 1 (upregulated) or log₂FC ≤ −1 (downregulated) with FDR < 0.05 were considered significantly differentially expressed.

), and 18 (11 significantly upregulated and 7 significantly downregulated) under QM saline–alkali stress (QM300) (

Table 5. Quantitative information of 17 differentially expressed miRNAs in QMCK vs. QM300.

miRNA	FDR	log2FC	Regulated
tae-miR9653b	4.28 × 10−18	5.961465227	up
tae-miR531	4.38 × 10−11	3.936354267	up
tae-miR9777	4.82 × 10−11	5.70173093	up
tae-miR7757-5p	1.11 × 10−8	3.112017494	up
tae-miR160	0.001282849	1.902737872	up
tae-miR398	0.001553477	2.286646879	up
tae-miR9662a-3p	0.001553477	−1.822318816	down
tae-miR9662b-3p	0.001553477	−1.818997721	down
tae-miR5384-3p	0.003151427	4.238668997	up
tae-miR9779	0.006422972	2.590190662	up
tae-miR167a	0.009105099	1.560676913	up
tae-miR9772	0.030616515	−1.588044376	down
tae-miR9674a-5p	0.034692328	−1.5264112	down
tae-miR164	0.038316711	−1.336902019	down
tae-miR408	0.038316711	−1.5509308	down
tae-miR1135	0.038316711	1.855655843	up
tae-miR171a	0.044710489	−1.308293764	down

Note: The standard definition of screening method for homology differentially expressed genes. FDR = False Discovery Rate (controls false positive proportion); log₂FC = log₂ fold-change (log₂-transformed ratio of expression in stress group vs. control group); log₂FC ≥ 1 (upregulated) or log₂FC ≤ −1 (downregulated) with FDR < 0.05 were considered significantly differentially expressed.

). Four miRNAs (miR9653b, miR5384-3p, miR9777, and miR531) showed consistent upregulated expression trends across both varieties and all stress concentrations. Among them, miR9653b was significantly upregulated by 6.8-fold in MS150 compared with the control group. In addition, miR9663-5p showed the most significant downregulation, which was only detected between MSCK and MS150. In conclusion, the number of saline–alkali stress-responsive miRNAs in QM were higher than those in MS, and the number of upregulated miRNAs was greater than that of downregulated ones in both the varieties, with MS having a higher proportion of upregulated miRNAs.

3.4. Venn Diagram Analysis of Differential miRNAs

There were two miRNAs (miR159a and miR159b) whose expression changes were consistent between the low saline–alkali stress group and the high saline–alkali stress group of MS. There were six miRNAs (miR9662a-3p, miR9662b-3p, miR7757-5p, miR164, miR408, and miR171a) whose expression changes were consistent between the low saline–alkali stress group and the high saline–alkali stress group of QM. There was one miRNA (miR9674a-5p) in the MS high saline–alkali stress group and QM high saline–alkali stress group, and four miRNAs (miR9653b, miR5384-3p, miR9777, miR531) in the MS low saline–alkali stress group, the MS high saline–alkali stress group, the QM low saline–alkali stress group, and the QM high saline–alkali stress group with the same changes in expression (Figure 3).

The overlapping regions were the common differences of miRNAs in the stress groups with respect to the relatively good control group.

3.5. Prediction of Differentially Expressed miRNA Target Genes

The functions were predicted for 125 differential miRNAs in MS150, 183 differential miRNAs in MS300, 123 differential miRNAs in QM150, and 191 differential miRNAs in QM300. Based on comparison of the above predicted data and analysis results, the predicted target gene functions were related to relevant regulatory mechanisms in plants under stress (

Table 6. Prediction of target genes of differentially expressed miRNAs.

MiRNA	Target Gene ID	Functional Annotation of Target Gene
tae-miR9653b	TraesCS1A03G0593600	Signal transduction mechanisms
	TraesCS1A03G0593400	Protein phosphatase 2C
tae-miR5384-3p	TraesCS1B03G1191600LC	Fatty acid biosynthetic process
	TraesCS2B03G0741100LC	Cytochrome P450
	TraesCS2A03G0619100	Oxidoreductase activity
	TraesCS1A03G1019800	Ribosomal structure and biogenesis
	TraesCS2B03G0254600	Golgi apparatus
	TraesCS2A03G0189200	Plasmodesma
	TraesCS2B03G0508700	GH3 auxin-responsive promoter
tae-miR9777	TraesCS1B03G1058500LC	Nucleic acid binding
	TraesCS2A03G0582500	Cytoskeleton
	TraesCS2B03G0673900	Intracellular trafficking
	TraesCS1B03G0381100LC	Chloroplast thylakoid membrane
	TraesCS1A03G0021800LC	Zinc ion binding
tae-miR7757-5p	TraesCS1D03G1023000	ATP binding
	TraesCS1A03G1061600	Plant-type hypersensitive response
	TraesCS1D03G0081100	Defense response to bacterium
	TraesCS2B03G0070200	Protein phosphorylation
	TraesCS1B03G0024400	ADP binding
	TraesCS1D03G0763800	Nucleotide binding
tae-miR9779	TraesCS2B03G0144900	Carbohydrate binding
tae-miR171b	TraesCS1D03G0533400	DNA-binding transcription factor activity
	TraesCS1B03G0644600	Cell division
	TraesCS2B03G0956600	Coenzyme transport and metabolism
	TraesCS2A03G0861500	Chloroplast
	TraesCS1B03G1055400LC	Endonuclease activity
	TraesCS1D03G0929400LC	Aspartic-type endopeptidase activit
	TraesCS1B03G0919400LC	RNA-directed DNA polymerase activity
tae-miR531	TraesCS1B03G0826000	Cell surface receptor signaling pathway
	TraesCS1D03G0484800LC	Amino acid transport and metabolism
	TraesCS1B03G0675000	Posttranslational modification
	TraesCS1A03G0581800	Inorganic ion transport and metabolism
	TraesCS1D03G0554400	Leaf morphogenesis
	TraesCS2A03G1243900	Protein deubiquitination
	TraesCS1B03G0631900	Function: structural constituent of cell wall
	TraesCS1B03G0873600LC	Cytokinin-activated signaling pathway
	TraesCS1D03G0039100	Protein kinase binding
	TraesCS1D03G0268300	Cell wall/membrane/envelope biogenesis
	TraesCS1B03G1150300LC	Plant-type secondary cell wall biogenesis
tae-miR159a	TraesCS2B03G0072700	Plant-type hypersensitive response
	TraesCS1A03G0795800	RNA processing and modification
	TraesCS1B03G0915500	Phosphoprotein phosphatase activity
	TraesCS1B03G0915400LC	RNA secondary structure unwinding
	TraesCS2A03G0995200	Transcription
	TraesCS1B03G0874000	Flower development
	TraesCS1D03G0731600	Nucleus

).

3.6. Target Gene GO Analysis

GO analysis of the target genes of differentially expressed miRNAs in MS150 showed that there were approximately 177 potential GO items with a p-value < 0.05, including 79 biological processes (44.6%), 56 molecular functions (23.7%), and 42 cell components (31.7%) (Figure 4). There were 251 GO indicators in MS300, including 108 biological processes (43%), 62 molecular functions (24.7%), and 81 cellular components (32.3%) (Figure 5). There were 183 GO terms in QM150, including 86 biological processes (47%), 58 molecular functions (31.7%), and 39 cell components (21.3%) (Figure 6). There were 195 GO terms in QM300, including 68 biological processes (34.9%), 75 molecular functions (38.5%), and 52 cell components (26.6%) (Figure 7).

In the research on the regulation of biological processes in wheat, the regulatory effects of target genes were concentrated in biochemical processes, such as cell processes, metabolic processes, biological regulation, and response to stimulation. Target genes mainly include important life activities that can directly participate in the binding of various target proteins, catalytic activity, structural molecular activity, and transport activity. In the study of cytological components, target genes play an important role in cells, cell components, organelles, cell membrane components, and other life processes.

3.7. Target Gene KEGG Path Analysis

KEGG pathway enrichment analysis of differentially expressed miRNA target genes revealed distinct pathway preferences among different treatment groups (

Table 7. KEGG pathway involved in differential miRNAs target genes.

Group	Pathway ID	Description	p-Value	Num
MSCK vs. MS150	map00062	Fatty acid elongation	1.79 × 10−5	4
	map04120	Ubiquitin mediated proteolysis	0.000664309	4
	map00402	Benzoxazinoid biosynthesis	0.005640343	1
	map00360	Phenylalanine metabolism	0.005810484	2
	map00330	Arginine and proline metabolism	0.009651351	2
	map00380	Tryptophan metabolism	0.010881392	2
	map04075	Plant hormone signal transduction	0.124172984	2
MSCK vs. MS300	map00062	Fatty acid elongation	7.27 × 10−5	4
	map00740	Riboflavin metabolism	0.001220018	2
	map04120	Ubiquitin mediated proteolysis	0.002492667	4
	map00860	Porphyrin and chlorophyll metabolism	0.010060877	2
	map00360	Phenylalanine metabolism	0.01106802	2
	map00330	Arginine and proline metabolism	0.018222765	2
	map00380	Tryptophan metabolism	0.020494774	2
	map00970	Aminoacyl-tRNA biosynthesis	0.028821689	2
	map04626	Plant-pathogen interaction	0.169591242	2
	map04075	Plant hormone signal transduction	0.209316859	2
QMCK vs. QM150	map00062	Fatty acid elongation	1.25 × 10−5	4
	map04626	Plant-pathogen interaction	0.00110667	4
	map00360	Phenylalanine metabolism	0.004945555	2
	map00330	Arginine and proline metabolism	0.008229269	2
	map00380	Tryptophan metabolism	0.00928266	2
	map04075	Plant hormone signal transduction	0.108348672	2
QMCK vs. QM300	map00062	Fatty acid elongation	9.12 × 10−5	4
	map04120	Ubiquitin mediated proteolysis	0.003075697	4
	map04626	Plant-pathogen interaction	0.006877752	4
	map00860	Porphyrin and chlorophyll metabolism	0.011182066	2
	map00360	Phenylalanine metabolism	0.012297941	2
	map00330	Arginine and proline metabolism	0.020212244	2
	map00380	Tryptophan metabolism	0.0227212	2
	map00970	Aminoacyl-tRNA biosynthesis	0.031901179	2
	map04075	Plant hormone signal transduction	0.227054513	2
	map04016	MAPK signaling pathway—plant	0.42996678	1

). There were seven newly discovered pathways in the target gene-related expression and regulation pathway of miRNAs in MS150, including ubiquitin-mediated proteolysis, benzoxazinoid biosynthesis, tryptophan metabolism, and fatty acid elongation. There were ten newly discovered miRNA target gene pathways in MS300, including fatty acid elongation, ubiquitin-mediated proteolysis, porphyrin and chlorophyll metabolism, aminoacyl tRNA biosynthesis, and other metabolic pathways. Six pathways related to QM150 miRNA target genes were identified, including plant–pathogen interactions, phenylalanine metabolism, and arginine, proline, and tryptophan metabolism. There were ten pathways related to QM300 miRNA target genes, including ubiquitin-mediated proteolysis, plant hormone signal transduction, and the plant MAPK signalling pathway. The target genes of the four treatment groups jointly participated in fatty acid elongation, phenylalanine metabolism, plant hormone signal transduction, arginine, proline, and tryptophan metabolism, and other pathways. In the two wheat varieties, the number of signalling pathways related to the high saline–alkali stress was higher than that related to the low saline–alkali stress, including most other target genes and related information pathways.

3.8. Effects of Saline–Alkali Stress on the Activities of SOD, POD and CAT in Wheat Seedling Roots

With an increase in the saline–alkali concentration, the activities of SOD, POD and CAT in the two varieties decreased (Table S3). Compared to QMCK, the SOD activity of QM and MS decreased by 8.93% and 22.55%, 13.78% and 38.37%, respectively; The POD activity of QM decreased by 23.49% and 27.96% compared to QMCK, and that of MS decreased by 33.25% and 47.02% compared to MSCK; The CAT activity of QM decreased by 30.35% and 41.67% compared to QMCK at two concentrations, and the MS activity decreased by 34.92% and 53.26% compared to MSCK at two concentrations. The activities of SOD, POD and CAT in QM decreased less with the increase in saline-alkali stress than those in MS.

3.9. Effects of Saline–Alkali Stress on the Contents of AsA and DHA in Wheat Seedling Roots

Ascorbic acid (AsA) is one of the most abundant water-soluble antioxidants in plants, and its oxidized form DHA is dynamically interconverted with AsA via redox reactions. AsA and its oxidized form DHA are critical for the root’s health, particularly in environments where roots face mechanical stress, nutrient limitations, or toxic compounds; a high AsA/DHA ratio neutralizes these ROS to preserve root tip viability. The AsA content of the two varieties showed an increasing trend (Table S4). At the two concentrations, QM significantly increased compared to QMCK, with an increase of 106.72% and 196.91%, respectively, and MS increased by 69.05% and 135.48% compared to MSCK. The increase in AsA content in QM under high-concentration stress was greater than that in MS, indicating that QM maintains a higher AsA content under high saline–alkali stress. The DHA content of QM also showed an upward trend with an increasing saline–alkali concentration. At each concentration, DHA increased by 44.51% and 65.31%, respectively, compared to QMCK, and MS increased by 31.77% and 74.01%, respectively, compared to MSCK. The increase in QM at high concentrations was smaller than that in MS, and DHA accumulation was relatively low. The AsA/DHA ratios of both varieties exhibited an increasing trend. QM at high concentrations increased by 43.75% and 81.25%, respectively, compared to the control. MS increased by 28.57% and 35.74%, respectively, compared to the control at low concentrations. The AsA/DHA ratio of QM treated with the saline–alkali solution was higher than that of MS.

3.10. Effects of Saline–Alkali Stress on GSH, GSSG Content and GSH/GSSG Ratio in Wheat Seedling Roots

Glutathione (GSH) and its oxidized form GSSG are central to root stress tolerance, detoxification, and redox signaling, especially in harsh conditions. A high GSH/GSSG ratio preserves the thiol groups of these proteins, enabling proper root hair elongation. The GSH contents of the two varieties showed an upward trend (Table S5). At the two concentrations, QM increased significantly compared to QMCK by 21.15% and 82.74%, respectively, and MS increased by 18.84% and 38.86% compared to MSCK, respectively, indicating that QM maintained a higher GSH content under high salinity stress. The GSSG content of QM also increased with increasing salt and alkaline stress concentrations. At these two concentrations, the GSSG content increased by 15.24% and 48.83%, respectively, compared to QMCK, and the GSSG content of MS increased by 107.75% and 189.98%, respectively, compared to MSCK. Under high-concentration stress, the increase in GSSG content in QM was smaller than that in MS, and GSSG accumulation was relatively small. The GSH/GSSG ratios of the two varieties exhibited different trends. Compared to QMCK, QM increased by 5.68% and 23.24% at saline–alkali concentrations of 150 and 300 mmol/L, respectively. However, MS at saline–alkali concentrations of 150 and 300 mmol/L decreased significantly compared to MSCK by 42.70% and 52.19%, respectively. The GSH/GSSG ratio in QM was higher than that in MS.

3.11. Effects of Saline–Alkali Stress on the Activities of APX and GR in Wheat Seedling Roots

With the enhancement of saline–alkali stress, the APX activity of the two varieties showed an increasing trend, but the range of the increase differed between the two varieties. Compared to QMCK, the APX activity of QM increased by 6.05% and 43.53%, respectively. Compared to MSCK, MS increased significantly by 20.24% and 47.10%, respectively (Figure S1A). The results showed that the increase in the APX activity of QM was much lower than that of MS under low saline–alkali concentrations, but higher than that of MS under high saline–alkali stress. With increasing concentration, the GR activity of QM increased to 20.54% and 73.01% higher than that of QMCK, respectively, and the increase in MS was 20.23% and 32.46% higher than that of MSCK (Figure S1B). The increase in the GR content of QM under high-concentration stress was greater than that of MS.

3.12. Effects of Saline–Alkali Stress on O₂ Production Rate, Content of H₂O₂ and MDA, and Root Activity of Wheat Seedlings

Under saline–alkali stress at different concentrations, the H₂O₂, O₂⁻ and MDA contents of QM and MS were directly proportional to the saline–alkali concentrations (Figure S2A–C). At each concentration of saline–alkali stress, the magnitude of the increase in H₂O₂ and O₂⁻ contents was smaller in QM than in MS, demonstrating that QM maintained lower levels of these two reactive oxygen species (ROS) under such stress. Across all saline–alkali stress concentrations, QM exhibited a less pronounced increase in H₂O₂ and O₂⁻ contents compared to MS, which was consistent with QM sustaining lower accumulation of these ROS. The MDA content in QM was significantly increased by 88.20% and 137.64% compared to that in QMCK at the two concentrations, respectively. MS increased significantly with increasing saline–alkali concentration. At these two concentrations, MS increased significantly compared to MSCK, with increases in amplitude of 188.46% and 230.22%, respectively.

Inhibition of root growth and development is the early and most prominent expression in plants under saline–alkali stress, and root activity is a key indicator for evaluating the adaptability of plants to stress. The root activities of the two varieties decreased with an increase in saline–alkali content and were significantly lower than those of the control group under the same saline–alkali content (Figure S2D). At the two tested concentrations, the root activity of QM decreased by 57.81% and 75.02% relative to QMCK, while that of MS decreased by 74.93% and 94.03% compared to MSCK, respectively. The magnitude of the decrease in root activity of QM under saline–alkali stress was significantly smaller than that of MS, confirming that QM maintained higher root activities under such stress.

4. Discussion

MiRNAs can regulate the normal growth and differentiation of plant organisms, embryonic development and growth, and other biochemical processes, as well as the regulatory response to external stress or external stress signals [16]. In our study, 56 miRNAs were identified in the two wheat varieties under different alkali stresses, and 4 miRNAs (miR9653b, miR5384-3p, miR9777, and miR531) showed a common trend.

MiR159 targets the MYB transcription factor [33]. The transcription factor R2R3 MYB is involved in plant growth and stress response [34]. MsMYB2L is also rapidly induced by NaCl, indicating that MYB is involved in the resilience response [35]. Similarly, BnaMYB21, BnaMYB141 and BnaMYB148 are involved in the regulation of salt tolerance [36]. Our data also showed that miR159 was not expressed in QM but was promoted in MS (Table 2 and Table 3), thus reducing the expression of target genes by encoding MYB-related transcription factors in MS, leading to different resistances between varieties.

MiR164 is a conserved miRNA unique to plants, and its target gene is primarily the NAC transcription factor [37]. Induction by heavy metal ions, radiation, and other factors can significantly upregulate the expression of miR164a, miR164c, and miR164d in hybrid rice [38]. Compared to other saline-alkali-sensitive varieties, miR164a, miR164c, and miR164d gene expression in saline–alkali-tolerant cotton was upregulated under saline–alkali stress [39]. Our experiments also showed that QM had a higher level of miR164 (Table 4 and Table 5). Thus, miR164 and its target gene, NAC, may be involved in regulating the normal growth and development of plants and in stress resistance.

MiR171 is a highly conserved miRNA gene family and one of the oldest miRNA families in the plant kingdom [40]. The target genes of different members of the same miRNA family are the same [41]. Plants overexpressing miR171 show phenotypic changes, such as reduction of lateral branches and shortening of the main roots [42]. In addition, overexpression of miR171 reduces salt tolerance in rice [43]. These findings suggest that differences in miR171 expression may be linked to oxidases, which play important roles in promoting biosynthesis, metabolism, detoxification, and stress-resistant growth in plant cells [44]. In addition, our data showed that under high saline–alkali stress, miR171a was downregulated in QM seedlings (Table 5), and miR171b was upregulated in MS seedlings (Table 2); thus, QM wheat seedlings had relatively longer roots (Table 2). Therefore, the target genes of miR171a and miR171b in the miR171 family include the GRAS family transcription factors and different wheat varieties may be an important factor in the formation of different saline–alkali tolerances.

MiR5384 is a drought-responsive miRNA in wheat [45]. Under drought stress, miR5384-3p affects transport and cellular redox homeostasis [46]. We predicted that the target of miR5384-3p is CYP450 (Table 6). CYP450 inhibitors are multifunctional [47]. The GRAS protein family is a plant-specific transcription factor that regulates the normal development of plant root organs, meristem formation, plant signal pathway transduction, stress resistance responses, and other plant growth and development processes [48]. Downregulation of OsCYP707A7 induces an increase in the ABA content and antioxidant enzyme activity in rice [49]. The upregulated expression of CYP85A1 in spinach improves the drought resistance of transgenic tobacco [50]. Therefore, we speculated that miR5384-3p may respond to antioxidant stress in wheat by regulating the target gene CYP450 and improving the salt–alkali resistance of wheat.

MiR408 is a highly conserved miRNA in plants that targets genes encoding copper-containing proteins [51]. An increase in miR408 gene expression further enhances the tolerance in plant cells to salt, low temperature, and oxidative stress [52]. Overexpression of miR408 enhances drought tolerance in chickpea [53]. Compared to the wild type, overexpression of miR408-3p (OX-amiR408) in transgenic cowpeas increased chlorophyll content, decreased cell H₂O₂ levels, and showed stronger drought resistance and salt tolerance [45]. Our results also showed that miR408 was not expressed in MS but was downregulated in QM (Table 4 and Table 5). In summary, miR408 may play a role in saline–alkali tolerance by regulating chlorophyll and ROS metabolism, which is an important reason for the differences in saline–alkali tolerance in wheat varieties.

MiR1135 can be considered a putative regulator of gene expression at the protein level. miR1135 is upregulated under saline–alkali stress, and its target is the MAPK signalling pathway. MAPK is a multifunctional signalling molecule that interacts with ROS and hormones to form an adaptive response [54]. ROS are important and common messengers that are generated under various environmental pressures. It also activates several MAPKs [55]. ABA-activated MAPK components are also activated by ROS, indicating that ABA and active oxygen may polymerise at the MAPK level to regulate stomatal closure [56]. miR1135 was highly expressed in QM under high saline–alkali stress and was not expressed in the other three treatment groups (Table 5). Thus, the expression of target genes encoding MAPK pathway-related transcription factors in QM was reduced, leading to differences in saline–alkali resistance among the varieties. In conclusion, miR1135 may make QM more tolerant to saline–alkali stress than MS by activating the expression of MAPK-related transcription factors in ROS in wheat.

In summary, the differential expression of many miRNAs (miR5384-3p, miR408, and miR1135) is related to ROS production, which may be involved in the differences in salt and alkaline tolerance between the two cultivars. ROS between plant cells is maintained at a low level under natural conditions [57], but various environmental pressures disrupt the balance between ROS production and elimination, leading to the continuous accumulation of ROS [58]. High ROS levels are cytotoxic [59]. ROS can seriously damage normal metabolism through oxidative damage to carbohydrates, lipids, proteins, and nucleic acids, causing damage to the membrane system and increasing MDA content [60]. Therefore, MDA is considered as a reliable indicator of oxidative stress [61]. In this study, the rate of formation of O₂⁻ and H₂O₂ content gradually increased with the increase of treatment concentration, which was consistent with the increase in MDA content under saline–alkali stress (Supplementary Figure S2). In a highly stressful environment, green plants can increase their antioxidant levels and antioxidant enzyme activities to eliminate ROS [62]. SOD is the first line of defence against oxygen free radicals. SOD catalyses O₂⁻ into O₂ and H₂O₂. Subsequently, H₂O₂ is decomposed into H₂O and O₂ by APX to protect organelles and cell membranes from damage by active oxygen. Under saline–alkali stress, the activities of SOD, POD, and CAT in the two wheat varieties with different saline–alkali tolerances showed a significant decreasing trend (Supplementary Table S2) owing to the increase in saline–alkali content. Under saline–alkali stress, the reaction of the enzymatic defence systems of different salt-tolerant varieties in the same plant is also quite different, which further indicates that the higher salt tolerance in plants is due to the improvement in protective enzyme activity.

In recent years, research has mainly focused on changes in AsA–GSH cycle activity under environmental stress as a good indicator of plant stress levels [63]. Various components of the AsA–GSH cycle have been detected in the cytoplasm, chloroplasts, mitochondria, and peroxidase [64]. The AsA–GSH cycle is an important and effective pathway for removing H₂O₂ from plants using AsA and GSH [65]. Transgenic plants over-expressing key AsA–GSH cycle enzyme genes can improve stress resistance by increasing AsA and GSH levels [66]. In the AsA–GSH cycle, APX uses AsA as the reductant to catalyse the reduction of H₂O₂ to H₂O. The root system of the salt-tolerant variety QM showed higher APX activity (Supplementary Figure S1), indicating that under saline–alkali stress, QM can effectively control ROS levels by increasing APX activity, thereby limiting the oxidative tissue damage caused by saline–alkali stress. APX uses AsA to process H₂O₂ and generate DHA and MDHA. As an electron donor, glutathione (GSH) participates in the transformation of DHA into AsA. GR catalyses the oxidation of GSSG to regenerate GSH. The results showed that under saline–alkali stress, the root GR activity of the salt-tolerant variety QM significantly increased, and the GR activity was higher (Supplementary Figure S1). Therefore, increased GR activity plays a key role in the antioxidant stress response in wheat. The increase in GR activity explains the increase in AsA/DHA and GSH/GSSG ratios. These results indicate that the increase in APX and GR enzyme activities may be one of the reasons why the roots maintained high levels of AsA and GSH under saline–alkali stress.

5. Conclusions

This study systematically analysed miRNA expression profiles and ROS metabolism in two wheat varieties under saline–alkaline stress. Saline–alkali stress (150/300 mmol·L⁻¹) induces differential expression of miRNAs in the roots of tolerant (QM) and sensitive (MS) wheat varieties. Four core miRNAs (miR9653b, miR5384-3p, miR9777, and miR531) show consistent expression trends across both the varieties and stress concentrations. Among these, miR5384-3p targets the cytochrome P450 pathway, while miR408 (upregulated in QM only) and miR1135 (highly expressed in QM under high stress) regulate the plant hormone signal transduction and the MAPK pathway, respectively. These miRNA-mediated pathways collectively modulate the antioxidant system: QM maintains higher activities of APX and GR (key enzymes in the AsA-GSH cycle), elevated levels of non-enzymatic antioxidants (AsA, GSH), and balanced AsA/DHA and GSH/GSSG ratios, which reduce ROS (O₂⁻, H₂O₂) accumulation and MDA-induced oxidative damage. In contrast, MS exhibits weak activation of these miRNA pathways, leading to reduced antioxidant capacity, excessive ROS accumulation, and severe growth inhibition (e.g., reduced root length, root activity). This coordinated interplay between miRNA regulation, redox homeostasis, and stress tolerance is the core finding of our study.

Future research should focus on: (1) Functional verification of core miRNAs (miR5384-3p, miR408, miR1135) using genetic engineering techniques (e.g., overexpression or CRISPR/Cas9-mediated knockout) to confirm their roles in saline–alkaline stress response; (2) exploration of the interaction between miRNA target genes (e.g., CYP450, MAPK) and ROS scavenging systems at the protein level; (3) development of molecular markers based on core miRNAs for breeding saline-alkali-tolerant wheat varieties.