Genome-wide analysis of Phaseolus vulgaris L. miR172 gene family members under heavy metal stress

Abstract

Background

Phaseolus vulgaris L. is a vital source of protein, vitamins, and minerals, significantly addressing nutritional deficiencies. P. vulgaris L., like other plant species, demonstrates considerable susceptibility to heavy metal stress, a significant environmental challenge. MicroRNAs (miRNAs) and their target genes play a crucial role in enabling plant responses to stress. This study investigated the role of the miR172 gene family, which is essential for growth and development and is among the first miRNAs identified in plants under heavy metal stress in common bean plants. By employing bioinformatics and experimental methods, we sought to gain insights into its regulatory mechanisms and potential roles in enhancing stress tolerance.

Results

Bioinformatics analysis identified six miR172 genes in the P. vulgaris L. genome on five different chromosomes. The precursor sequences of Pvul-miR172 exhibited a characteristic root-loop structure, and the 19-nucleotide (nt) mature sequences were conserved across various species, categorizing them into three distinct phylogenetic classes. Elements responsive to stress were identified in the promoter regions of Pvul-miR172. Gene Ontology (GO) analysis revealed that eight target genes associated with Pvul-miR172 are involved in carrier activity and binding, which is crucial for stress response. Measured parameters included root and shoot length, cell membrane integrity, relative water content (RWC), and chlorophyll levels in two common bean genotypes. Exposure to heavy metal stress increased antioxidant enzyme activity and elevated proline (Pro), hydrogen peroxide (H₂O₂), and malondialdehyde (MDA) levels. The analysis of expression profiles for Pvul-miR172s and their target genes under heavy metal stress revealed diverse regulatory patterns. Principal Component Analysis (PCA) showed positive and negative correlations between Pvul-miR172s and their predicted target genes, indicating a complex regulatory interaction under heavy metal stress. The findings suggest that Pvul-miR172s contribute to increased tolerance to heavy metal stress, with effects differing by genotype.

Conclusions

Our findings suggest that members of the Pvul-miR172 gene family may play important roles in the common bean’s response to heavy metal stress. Expression levels of these genes and their targets varied depending on the type of heavy metal and genotype, with the G68 (tolerant) genotype being particularly prominent in stress tolerance. Stress-related cis-regulatory elements and differential expression profiles under stress conditions suggest that this gene family may be involved in regulatory networks associated with the stress response. These results provide fundamental information for developing transgenic lines with stress tolerance through Pvul-miR172s and their targets using miRNA-based approaches such as amiRNA, STTM, and miPEP.

Introduction

The increasing climate crisis, the decline in biodiversity, and the economic problems faced create global challenges to people’s access to food. It is estimated that by 2030, 600 million people will lack sufficient access to essential food and will face hunger [1, 2]. It is thought that legumes with rich nutritional content can contribute to solving the problem of nutrient deficiency [3]. Legumes are a vital food source, as they contain metabolites with phytochemical and antioxidant activity, as well as essential components such as protein, minerals, and vitamins necessary for human health [4]. Common beans (Phaseolus vulgaris L.), the most widely produced legumes in recent years, play a crucial role in ensuring food and nutrition safety due to their low fat, high protein, and fiber content [5]. Over the ten years (2012–2022), global common bean production increased from 24 million to 28 million tons. However, when the world’s common bean yield is examined, it is observed that a decrease occurs [6]. Biotic and abiotic stresses are among the primary factors that contribute to the decline in common bean yield [7]. Extreme heat, drought, soil acidity, salinity, and heavy metal toxicity are significant abiotic stresses [8, 9]. The advancement of industrialization and natural and anthropogenic activities causes increased heavy metal concentrations in agricultural lands, negatively affecting plants’ morphological processes and human health [10, 11]. Heavy metal stress is one of the most damaging abiotic stresses in plants, resulting in stunted plant growth, reduced biomass production, chlorosis, necrosis, a limited number of seeds, wilting, and leaf yellowing, which significantly affects crop yield and quality. It can result in the slowing of plant growth and plant death. Heavy metal ions entering plants cause oxidative stress by producing reactive oxygen radicals, disrupting cell membrane permeability, photosynthesis, and ion homeostasis [12, 13]. Heavy metals also have adverse effects on common beans, including deterioration of nodule structure, inhibition of nitrogenase activity, and reduced water and nutrient uptake [14].

Since biological systems have dynamic structures by nature, they try to adapt by responding to environmental and genetic changes [15]. Biochemical, physiological, molecular, and morphological changes occur in plants exposed to abiotic stresses [16]. Plants have evolved numerous mechanisms to respond to various stresses in the face of different stressors to which they are exposed [17, 18]. While plants can prevent the entry of heavy metals into the cell with the help of organic anions, such as malate and oxalate, they are also neutralized within the cell through the action of transmembrane transporters and cellular receptors, and subsequently transported to organelles, including vacuoles [19]. In another mechanism, miRNAs (microRNAs) act as post-transcriptional regulators and play a crucial role in stress responses [20]. It has been observed that miRNA biogenesis occurs in response to the needs of plant development processes and the challenges posed by environmental stressors [21]. Plant miRNAs have an important function in the abiotic stress responses to which plants are exposed through post-transcriptional gene editing [22]. Therefore, they are considered critical tools for enhancing tolerance to abiotic stress [23]. miRNAs play key roles in regulating important plant development events such as flowering type, meristem structure, and leaf development [24]. miR156 and miR172 also manage the flowering process through their target genes. The expression of miR156 is crucial for the juvenile period and plant development. When plants age, there is a decrease in the expression levels of miR156, while the expression levels of miR172 gradually increase [25–28]. In addition, miR172, which plays important roles in the developmental stages of plants, is found to be utilized in response to cold [29] and salt [30, 31] stress. It has also been observed that plants exposed to abiotic stresses, such as high temperatures [32] and drought [33], respond differently. Transgenic miR172 lines can create tolerance to these stresses. In addition, miR172 gene family members are composed of arsenic (As) [34], copper (Cu) [35], mercury (Hg) [36], cadmium (Cd) [37, 38], chromium (Cr) [39] and lead (Pb) [40] have been shown to respond under heavy metal stresses that have an effect that helps the plant to provide tolerance.

Heavy metals’ uptake, displacement, and mobility into plant cells and tissues vary mainly depending on the heavy metal concentration, type, and plant species [41]. It is also known that miRNAs can be regulated by species and abiotic stress-specific [42]. Therefore, more research is needed on miR172, which reacts differently to heavy metals. However, more studies are needed on the role of the miR172 gene family in heavy metal stress in common beans, which has important roles in plant growth and development processes. Only the expression status of miR172 under manganese (Mn) toxicity in common beans has been studied [43]. As a result of the literature review, no study on the role of the miR172 gene family in As, Cd, nickel (Ni), and Pb heavy metal stresses in common beans could be detected in previous studies. In this study, the chromosomal localization of the P. vulgaris L. miR172 gene family, secondary structure estimations, conservation levels, cis-acting elements, and phylogenetic analysis of miR172s were performed, along with a genome-wide characterization of target genes using gene ontology (GO) analysis, utilizing various databases and bioinformatics tools. In addition, physiological parameters such as the effect of four different heavy metals on root and shoot lengths in common beans, cell membrane damage, relative water content (RWC), and chlorophyll content (CC) were examined. In addition, biochemical parameters such as hydrogen peroxide (H₂O₂), malondialdehyde (MDA), antioxidant enzyme, and proline (Pro) concentrations were measured under heavy metal stress conditions. In addition, molecular analyses were performed by examining the change in the expression levels of miR172 gene family members and target genes in common beans exposed to As, Cd, Ni, and Pb stress. The responses of miR172 and its target genes under various heavy metal stress conditions may provide valuable information for miRNA-based applications aimed at creating stress-resistant variants.

Materials and methods

Identification of P. vulgaris L. miR172 gene family members

Sequence information of P. vulgaris L. miR172 gene family members was obtained using sRNAanno and PmiREN 2.0 databases [44, 45]. The chromosomal locations of the miR172 gene family members were scanned on the P. vulgaris L. (https://phytozome-next.jgi.doe.gov/info/Pvulgaris5_593_v1_1) genome with the help of the BLAST tool in the Phytozome v13 [46] database. Chromosome information was visualized using the MapGene2Chrom (MG2C v2.1- http://mg2c.iask.in/mg2c_v2.1) web tool [47]. The RNAfold WebServer web tool was used to predict the secondary structures of the Pvul-miR172 gene family members [48].

Conservation, phylogenetic, and Cis-acting element analysis of Pvul-miR172 gene family members

With the help of the SeqLogo tool in TBtools [49], conserved regions between mature sequences of the Pvul-miR172 gene family members were visualized. Pioneer sequence information of Arabidopsis thaliana, Glycine max, Medicago truncatula, Oryza sativa, P. vulgaris L., Vigna radiata, and Zea mays miR172 genes was obtained from the sRNAanno database. Sequence information was aligned using the ClustalW tool [50]. An unrooted phylogenetic tree was constructed using the Maximum Likelihood (ML) estimation method with 1000 bootstrap replicates in MEGA 11 [51] to assess the relative genetic relationships among the analyzed sequences without inferring evolutionary direction. Rooting was not applied because the primary objective was determining sequence similarity and clustering patterns. The constructed phylogenetic tree was visualized using the web-based tool Evolview v2 [52]. The 2000 base pairs (bp) upstream region of the precursor sequences of the Pvul-miR172 gene family members was obtained from the PmiREN database. The PlantCARE [53] database was used to estimate the cis-acting elements in the promoter region. Considering previous common bean studies, cis-effective elements were categorized according to their functions [54–56].

GO Analysis of Pvul-miR172 gene family target genes

Pvul-miR172 target genes were aligned with the BLAST tool in the UniProt [57] database, and GO explanations were obtained. In addition, the GO information of the target genes in the Phytozome v13 database was also obtained. All results were combined to create a final GO analysis result. The analysis results were graphed using the ChiPlot web tool (https://www.chiplot.online/).

Plant material, growth conditions, and stress applications

This study conducted a preliminary trial to determine tolerant and sensitive genotypes at half-maximal effective concentration (EC50) and toxic dose in 29 common bean genotypes previously selected by single selection. Seeds of these genotypes were first incubated in 70% EtOH for 5 min and then washed with dH₂O to remove alcohol. It was then subjected to surface sterilization in a solution containing 1% NaOCl for 15 min. Genotypes were subjected to germination using double-layered blotting paper in petri dishes, given ½ Hoagland basal salt solution [58] and heavy metal doses. Heavy metal mixtures were prepared for this purpose in the specified doses below. All petri dishes were subjected to the germination process for 7 days under controlled conditions in a plant growth cabinet at 25 °C with 250 nmol of 16/8 h photoperiod and heavy metal doses of 10-20-30 mg/L for As, 100-200- 300 mg/L Cd, 300-600-900 mg/L for Ni, and 300-400-500 mg/L for Pb were applied. At the end of the seventh day, phenotypic changes were observed in the genotypes. Based on these observations, tolerant (G68) and sensitive (G53) common bean genotypes were selected.

Before the applications, seeds of the selected genotypes were incubated in 70% EtOH for 5 min and then washed with dH₂O to remove alcohol. It was subjected to surface sterilization in a 1% NaOCl solution for 15 min. Seeds were grown up to the three-leaf stage in germination vessels containing perlite in a plant growth cabinet with a temperature of 25 °C and a photoperiod of 16 h. Until this period, the plants were watered in equal amounts with a 1/4 Hoagland solution every 2 days. Then, the healthiest plants that were phenotypically similar were selected and transferred to the hydroponic environment. The selected seedlings were grown in the same manner until they reached the five-leaf stage. Then, the heavy metals NaAsO₂, CdSO₄, NiSO₄, and Pb (NO₃)₂ were applied to the plants at a concentration of 50 mM. The experiment was set up according to the coincidence test plan with three iterations and 10 plants in each iteration. On the seventh day of application, leaf and root samples were taken for biochemical, physiological, and molecular analysis. For molecular analysis, plant tissue samples were collected using liquid nitrogen and stored at -80 °C until RNA isolation studies were performed.

H₂O₂, MDA, and antioxidant activity (APX, POD, and SOD) analyses

H₂O₂, MDA, and antioxidant activity analyses were performed according to the methods described by [59]. Samples collected for MDA analysis were homogenized with 5 ml of 5% TCA solution and centrifuged at 5000 g for 15 min. After mixing 2 mL of the supernatant with 2 mL of MDA reaction solution, the mixture was incubated at 95 °C for 45 min. After the samples were cooled on ice for 10 min, absorbance measurements were made at 550, 532, and 600 nm wavelengths. The MDA concentration was calculated using the absorbance curve obtained using the 155 mmol L^-1 cm^-1 exclusion coefficient of the thiobarbituric acid reactive substance. Samples collected for H₂O₂ analysis were homogenized with 5 ml of 5% TCA solution and centrifuged at 1200 g for 15 min. The supernatant was incubated for 60 min at 4 °C. H₂O₂ reaction solution was added to the samples, and absorbance measurements were taken at a wavelength of 390 nm. The amount of H₂O₂ was measured using a standard calibration curve at different concentrations. For antioxidant activity analysis, 180 µL of APX (EC 1.11.1.11) solution is added to the samples, and measurements are taken 3 times at 290 nm with 1 m intervals. POD (EC 1.11.1.7) activity was measured by adding 145 µL of POD solution to 5 µL of the sample at a wavelength of 470 nm for 1 min, with measurements taken every 15 s. For SOD (EC 1.15.1.1) analysis, a 100 µM riboflavin solution and 170 µL SOD solution were added to the samples, and the absorbance was measured at a wavelength of 560 nm.

Proline (Pro) contentProline (Pro) content

The Pro content was determined according to the method applied by Xiang et al. [60]. After centrifugation, samples were homogenized with 3% sulfosalicylic acid and treated with toluene. Then, absorbance measurement was taken at 520 nm wavelength. A standard curve based on pure Pro was used to evaluate the Pro level in leaf tissues.

Cell Membrane Damage (CMD%)

CMD was determined according to the method described by Lutts et al. [61]. The CMD (%) was calculated using the following formula:

Relative Water Content (RWC%)

Four leaf samples were selected from stress-treated plants to determine the RWC. After measuring their fresh weight (FW), they were soaked in distilled water for 4 h, and their turgid weight (TW) was recorded. Afterward, the samples were dried in an 80 °C oven for 2 days to determine their dry weight (DW). The RWC (%) was calculated using the formula described by Barrs and Weatherley [62]:

Chlorophyll Content (CC)

CC was determined by selecting five random plants and measuring them using the SPAD-502 (Chlorophyll Meter; Konica Minolta, Tokyo, Japan) device.

RT-qPCR Analysis of Pvul-miR172 gene family members and target genes

Total RNA isolation from leaf samples was performed using Hibrizol. cDNA synthesis was performed according to the protocol of the CanvaxTM cDNA synthesis kit (PR008). The cDNA synthesis of miRNAs was conducted using primers designed explicitly for miRNAs, following the method of Chen et al. [63] (Table 1). In the RT-qPCR reactions, miRNA-specific forward primers and a universal reverse primer were used. The U6 gene was the reference gene [64]. The detection and quantification of miRNAs were performed according to the protocol specified by Varkonyi-Gasic et al. [65]. The miRNA cDNAs used in the RT-qPCR analyses were diluted to 1/2, 1/4, and 1/8 ratios for each sample. All RT-qPCR reactions were performed in triplicate, including both biological and technical replicates. For miRNA RT-qPCR, the mix was prepared with a total volume of 10 µL, including 2 µL of miR-cDNA (1/2, 1/4, and 1/8), 0.5 µL of primer (forward + reverse), 5 µL of SYBR Green Master Mix, and 2.5 µL of dH₂O. The reaction consisted of a pre-denaturation step at 65 °C for 5 min, followed by denaturation at 95 °C for 20 s, annealing at 57.5–60 °C for 35 s, and extension at 72 °C for 5 s over 50 cycles.

Table 1.

Primers used for Pvul-miR172 in RT-qPCR analysis. A stem-loop RT primer was designed for each Pvul-miR172 sequence, and the corresponding forward primer was designed using this primer. As required by the stem-loop structure, a universal primer as the reverse primer was used

miRNA	Stem-loop RT Primer	Forward Primer	Reverse Primer
Pvul-miR172a-3p	GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACCTGCAG	CGGCGGGGAATCTTGATGATGC
Pvul-miR172c-5p	GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTGTGAA	TTAGGGCAGCAGCATCAAGAT
Pvul-miR172d-5p	GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTGTGAA	TAGCGGGGAGCATCATCAAGAT	GTGCAGGGTCCGAGGT
Pvul-miR172f-5p	GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTGTGAA	TCGGCGGCAGCAAGATCAAGATT
U6	GAGAAGATTAGCATGG	CACGAATTTGCGTGTCATCCTT

The target genes of Pvul-miR172 were predicted using the psRNATarget database [66] with default settings. Among the predicted genes, those reported to be related to heavy metal stress and whose expression levels change under heavy metal stress conditions in P. vulgaris L [67]. were screened. Matching genes were assigned as targets for members of the Pvul-miR172 gene family, establishing the miRNA-target gene relationship. Primers specific to Pvul-miR172 target genes were designed (Table 2). The cDNAs used in RT-qPCR analyses were diluted at a 1/10 ratio. All RT-qPCR reactions were performed in triplicate. The necessary components and reaction program for RT-qPCR are given below. The RT-qPCR mix was prepared with a total volume of 10 µL, comprising 2 µL of cDNA (100 ng), 0.5 µL of each primer (forward and reverse), 5 µL of SYBR Green Master Mix, and 2.5 µL of dH₂O. The reaction consisted of a pre-denaturation step at 65 °C for 5 min, followed by denaturation at 95 °C for 20 s, annealing at 57.5–60 °C for 35 s, and extension at 72 °C for 5 s over 40 cycles.

Table 2.

Primers used in RT-qPCR analysis of Pvul-miR172 target genes. These genes were selected from among heavy metal-related genes due to their functions in pathways related to metal uptake, transport, and detoxification

Target Gene	Forward Primer	Reverse Primer	Function
Phvul.001G206700.1	GTACAAGTCCGTGCTGAAGAT	GGATGCTTTGATGTCTCGTTTG	ABCTransporter C Family Member 14-Related
Phvul.002G016900.1	GCCTAAGGCTCATCTGGTAAAT	GGCTCAGTAGTCTCAAGGTTTC	Floral Homeotic Protein Apetala 2
Phvul.002G291200.1	CGAGACATGGACCTGATGAAA	CTTCCACACACCAACCTCATA	Ubiquitin-Conjugating Enzyme E2 // Subfamily Not Named
Phvul.006G137900.1	CTTCTGGTACTCCTCCAACATC	TGGCACAGCGTAAGGAAA	Solute Carrier Family 35 // Subfamily Not Named
Phvul.007G240200.1	AGCATGGTGGGCAGAATAAG	GATGAGACAGCCAGAAGAAGAG	Ap2-Like Ethylene-Responsive Transcription Factor Smz-Related
Phvul.008G266700.1	GGAGAATTGGTGAGGGTACTTC	CATTGGGCATGATCCTTGTTG	Ring Finger Domain-Containing
Phvul.009G036900.2	TGCCGTGTCGTTGTTCAT	GACCTGCTAGAGTGAGCAATAC	YNAI-Related Mechanosensitive Ion Channel // Subfamily Not Named
Phvul.011G207100.1	CCATCAGGGAGCCCATTTAC	TGCTCCTCCTCCTCATAACA	Protein NRT1/PTR Family 5.4

The primers used in the studies were designed using the Primer3 web tool (https://bioinfo.ut.ee/primer3-0.4.0/). The reactions used a Genious 2X SYBR Green Fast qPCR Mix (RK21204, ABclonal, Türkiye). The components used in the reactions were adjusted according to the ratios specified by the manufacturer. The RT-qPCR analysis was performed using the RotorGene Q Real-Time PCR System.

Statistical analysis

The study was designed using a completely randomized experimental plan. The expression measurements of the miR172 gene family under heavy metal stress were normalized using the weighted average normalization method [68]. In contrast, the expression measurements of the miRNA target genes were normalized against the P. vulgaris L. U6 gene. The fold changes in gene expression were analyzed using the 2^-ΔΔCT normalization method developed by Livak and Schmittgen [69]. Using a correlation matrix, PCA was conducted to evaluate the relationship between stress groups and the variables being examined. PCA analysis was graphically interpreted using biplots. The means, standard deviations, and graphs of the data obtained from three biological and three technical replicates were visualized using GraphPad Prism 8.

Results

Genome-Wide identification of the Pvul-miR172 gene family members

In the P. vulgaris L. genome, six miR172 gene family members have been identified: Pvul-miR172a, Pvul-miR172b, Pvul-miR172c, Pvul-miR172d, Pvul-miR172e, and Pvul-miR172f. The nomenclature, mature sequence information, and chromosomal locations of the identified genes are given in Table 3. The length of all Pvul-miR172 mature sequences has been determined to be 21 nt.

Table 3.

Information about the P. vulgaris L. miR172 gene family members. The mature sequence lengths of this gene family members are 21 nt. Furthermore, these genes are distributed across five different P. vulgaris L. chromosomes

Gene Name	Mature Sequence (5’-3’)	Length (nt)	Chromosome No	Start Location	End Location
Pvul-miR172a	GGAATCTTGATGATGCTGCAG	21	PvChr1	51,848,885	51,849,015
Pvul-miR172b	GGAATCTTGATGATGCTGCAG	21	PvChr5	41,243,099	41,243,231
Pvul-miR172c	AGAATCTTGATGATGCTGCAT	21	PvChr7	11,039,332	11,039,432
Pvul-miR172d	AGAATCTTGATGATGCTGCAT	21	PvChr8	57,673,478	57,673,605
Pvul-miR172e	AGAATCTTGATGATGCTGCAT	21	PvChr11	8,168,544	8,168,680
Pvul-miR172f	AGAATCTTGATGATGCTGCAC	21	PvChr5	43,033,503	43,033,657

Members of the Pvul-miR172 gene family are distributed on five different P. vulgaris L. chromosomes (Fig. 1). Pvul-miR172a is located on chromosome 1, Pvul-miR172b and Pvul-miR172f genes are located on chromosome 5, Pvul-miR172c is located on chromosome 7, Pvul-miR172d is located on chromosome 8, and Pvul-miR172e is located on chromosome 11.

Fig. 1.

Chromosomal distribution of the Pvul-miR172 gene family members. The genes are on chromosomes 1, 5, 7, 8, and 11. Only Pvul-miR172b and Pvul-miR172f are located on PvChr5, while the other Pvul-miR172s are located on separate chromosomes. Furthermore, expect for Pvul-miR172c and Pvul-miR172e, the genes are located near the endpoints of the chromosomes

The secondary structures of the Pvul-miR172 gene family members were analyzed and visualized using the RNAfold WebServer. All Pvul-miR172 genes were found to be in the hairpin structure, which is necessary for miRNA formation (Fig. 2).

Fig. 2.

Secondary structures of the Pvul-miR172 gene family members. All genes show a hairpin structure. The sequences shown in orange represent the mature sequences. While the Pvul-miR172d and Pvul-miR172f exhibit a similar loop structure on their secondary structures, the Pvul-miR172e shows a larger loop structure than the others

Evolutionary relationship of the Pvul-miR172 gene family members

As a result, the conserved site analysis of the members of the Pvul-miR172 gene family revealed that 19 nucleotides of the 21-nucleotide mature sequences were conserved among all sequences (Fig. 3).

Fig. 3.

Conservation analysis of the Pvul-miR172 gene family members. This gene family members contain a mature sequence of 21 nt. The first and last nt of the sequences were not conserved across the different genes

To understand the evolutionary relationship of the miR172 genes, multiple sequence alignments were performed on the Pvul-miR172 gene family members with miR172 genes in six different organisms. A phylogenetic tree is then created. As a result of the analysis, three distinct groups were formed: A, B, and C (Fig. 4). The Pvul-miR172 genes other than Pvul-miR172f were included in group A. When the phylogenetic tree is examined, the Pvul-miR172a and Gma-miR172d genes, the Pvul-miR172b and Vra-miR172b genes, the Pvul-miR172c and Vra-miR172c genes, and the Pvul-miR172d and Gma-miR172h genes are grouped as neighboring genes in Group (A) Pvul-miR172f and Gma-miR172i appear as neighboring genes in Group (B) Again, Osa-miR172c and Zma-miR172c genes were identified as neighboring genes in this group. In group C, as in group B, members of the Osa-miR172 gene family and members of the Zma-miR172 gene family were included as neighboring genes. Members of the Ath-miR172 gene family are located only in group A.

Fig. 4.

Phylogenetic analysis of members of the Pvul-miR172 gene family. The phylogenetic tree was created in three classes using 40 different miR172 genes in seven organisms. Five miR172 genes were used from A. thaliana, represented by a green square; 11 from G. max, represented by an orange star; five from M. truncatula, represented by a light green star; four from O. sativa, represented by a gray round, six from P. vulgaris L. represented by a burgundy star, three from V. radiata represented by a dark blue star, and six from Z. mays represented by a purple tick sign

Estimation of Cis-acting elements of the Pvul-miR172 gene family members

Cis-acting elements located in the upstream region of members of the Pvul-miR172 gene family were analyzed. As a result of the analysis, it was found that cis-acting elements are mainly located on the Pvul-miR172c gene (Fig. 5). When the stress-related elements were examined, it was determined that MYB (34) was the most common element among the Pvul-miR172 genes. In addition, stress-related elements are mostly found on Pvul-miR172d (29). Box (24) was the most abundant element among the Pvul-miR172 genes when the elements involved in light responses were examined. In addition, the elements involved in light responses are mainly located on Pvul-miR172b (20). ERE (11) is the most abundant element among the Pvul-miR172 genes in hormone-sensitive elements. Hormone-sensitive elements were primarily found on Pvul-miR172c and Pvul-miR172f (12). When the elements associated with development were examined, it was determined that O2-site (five) was the most abundant element among the Pvul-miR172 genes. The gene with the most developmental elements was Pvul-miR172f (four).

Fig. 5.

Prediction of cis-acting elements of Pvul-miR172 gene family members involved in different processes. Elements were examined in four distinct categories: stress, light, hormone, and developmental elements. Genes with essential elements in this category are represented in a solid round, and genes without them are described in a hollow round

GO Analysis of Pvul-miR172 Target Genes

As a result of the GO analysis performed on the target genes of the members of the Pvul-miR172 gene family, it was understood that they are involved in various processes. Target genes are understood to transport certain substances across the transmembrane. They are also involved in DNA-binding and multicellular organism development processes with ATP. Among the target genes, the ABC transporter was involved in the most biological processes. It is followed by the UBC and NRT genes (Fig. 6).

Fig. 6.

GO analysis of Pvul-miR172 target genes. The biological processes in which the target genes are involved are represented in blue, the cellular components in which they are located are depicted in orange, and their molecular functions are in green

Growth, physiological and biochemical effects of heavy metal stress

Growth and physiological parameters

In both genotypes exposed to heavy metal stress, root and shoot lengths showed adverse changes compared to the control. When the root lengths were examined, the highest decrease was observed in both genotypes due to Cd stress (Fig. 7A). Root length under Pb stress in the G53 genotype and under As stress in the G68 genotype was measured to be longer than that under other heavy metals. Similarly, in both genotypes, shoot length was the stress that decreased the most after Cd stress (Fig. 7B). Ni treatment resulted in longer shoot length in both genotypes than in other heavy metal species.

Fig. 7.

Root and shoot lengths resulting from heavy metal stress. Lengths were compared in two different genotypes, and four heavy metal species were compared to those in the control group. A RL: root length (B) SL: shoot length

CMD exhibited negative effects under all heavy metal stress conditions, regardless of genotype (Fig. 8A). The highest increase in CMD occurred in both genotypes due to Cd stress. It was observed that the least rise in CMD resulted from Pb treatment. RWC decreased across all genotypes compared to the control group (Fig. 8B). While RWC was at the highest level in both genotypes under As stress, it was found to be at the lowest level in the G53 genotype under Pb and in the G68 genotype under Cd stress. When the CC was examined, the amount of chlorophyll decreased due to all heavy metal stresses (Fig. 8C). While the CC was the lowest in both genotypes due to Cd stress, the CC was the highest in the G53 genotype due to Ni treatment and in the G68 genotype due to Pb treatment.

Fig. 8.

Physiological changes that occur as a result of heavy metal stress. Physiological changes were compared between two different genotypes, and four types of heavy metals were compared to those in the control group. (A) CMD: cell membrane damage (B) RWC: relative water content (C) CC: chlorophyll content

Antioxidant enzymes exhibited different responses in both heavy metal species and genotypes. APX enzyme activity was significantly reduced compared to the control groups (Fig. 9A). Only in the G53 genotype did APX activity increase due to Pb treatment. The POD enzyme level was significantly increased compared to the control group (Fig. 9B). In both genotypes, Pb treatment resulted in the highest enzyme activity. The SOD enzyme was similarly increased as a result of heavy metal treatments (Fig. 9C). The highest level of SOD enzyme activity was observed as a result of Pb stress in the G53 genotype and As stress in the G68 genotype. Pro levels also mainly increased due to stress treatments (Fig. 9D). The highest increase was observed in the G53 genotype following Pb treatment and in the G68 genotype following Cd treatment. However, the amount of Pro in the G68 genotype decreased due to Ni and Pb treatment. The amount of H₂O₂ also mainly increased due to stress applications (Fig. 9E). While a result of As stress only in the G68 genotype, a decrease in H₂O₂ level occurred compared to the control group. The highest increase in H₂O₂ was observed in both genotypes due to Pb treatment. The amount of MDA varied according to genotype and stress types (Fig. 9F). While there was a decrease in the G53 genotype except for Pb stress, there was an increase in the MDA level in the G68 genotype except for Pb stress.

Fig. 9.

Biochemical changes that occur as a result of heavy metal stress. Biochemical changes were compared in two different genotypes and four different types of heavy metals compared to the control group (A) APX: ascorbate peroxidase (B) POD: peroxidase (C) SOD: superoxide dismutase (D) proline (Pro) (E) H₂O₂: hydrogen peroxide (F) MDA: malondialdehyde

Differences between genotypes were observed when physiological and biochemical parameters were examined after heavy metal stress. Root length under As and Ni stress and shoot length under Cd and Pb stress were longer in the G68 genotype. CMD occurred less in the G68 genotype, except for Pb stress. The amount of chlorophyll was higher in the G68 genotype under As and Pb stresses. The amount of SOD was found to be higher in the G68 genotype under As stress. The amount of Pro was higher in As and Cd stresses in the G68 genotype. The amount of MDA was higher in the G68 genotype, except for Pb stress. On the other hand, the root length was longer in the G53 genotype under Cd and Pb stresses. RWC was generally found to be higher in the G53 genotype. In addition, the amount of H₂O₂ was higher in the G53 genotype, except for As stress.

Quantitative real-time analysis of the Pvul-miR172 and target genes

The expression status of the Pvul-miR172 gene family members against two different genotypes and four different heavy metal stresses was analyzed. Pvul-miR172a expression increased under all stresses in the G53 genotype (Fig. 10A). The highest increase was as a result of As stress. In the G68 genotype, an increase in Pvul-miR172a expression was observed in Cd and Ni stresses compared to the control group, while the expression level decreased in As and Pb stresses. Pvul-miR172c expression increased at Ni stress and decreased at Cd stress in the G53 genotype (Fig. 10B). Similar expression levels were observed in other stresses, such as in the control group. In the G68 genotype, only Cd stress increased the expression of Pvul-miR172c, while a decrease occurred in other stresses. There was an increase in the G53 genotype of Pvul-miR172d expression, except for Cd stress (Fig. 10C). In the G68 genotype, Ni and Pb stresses increased the expression of Pvul-miR172d, while the expression level decreased due to As and Cd stresses. Pvul-miR172f expression increased in the G53 genotype only due to As stress, while other stresses caused a decrease in the expression level (Fig. 10D). In the G68 genotype, Pvul-miR172f expression increased in heavy metal stresses other than Pb stress.

Fig. 10.

RT-qPCR analysis of members of the Pvul-miR172 gene family under heavy metal stresses in G53 and G68 genotypes. (A) Pvul-miR172a (B) Pvul-miR172c (C) Pvul-miR172d (D) Pvul-miR172f

Some Pvul-miR172 genes showed differential expression patterns when heavy metal stresses were examined between the two genotypes. Under stress conditions, the expression levels of Pvul-miR172a and Pvul-miR172d were significantly upregulated in the G53 genotype, whereas the same genes were downregulated in the G68 genotype. Similarly, under Cd stress, the expression levels of Pvul-miR172c and Pvul-miR172f were downregulated in G53 but upregulated in G68. In contrast, under Ni stress, Pvul-miR172c expression was upregulated in G53 and downregulated in G68, whereas Pvul-miR172f showed the opposite trend, being downregulated in G53 and upregulated in G68. Under Pb stress, Pvul-miR172 gene family members showed a similar direction of expression in both genotypes compared to the control group, except for Pvul-miR172a. In Pb stress, Pvul-miR172a expression increased in the G53 genotype and decreased in the G68 genotype.

The ABC transporter gene exhibited an overall decrease in expression level due to heavy metal stress (Fig. 11A). Only in the G53 genotype did its expression increase due to As stress. AP2 gene expression decreased in both genotypes, except for As stress (Fig. 11B). UBC gene expression decreased in the G53 genotype except for As stress, while it increased in all heavy metal stresses in the G68 genotype (Fig. 11C). A similar expression profile was observed in the UAA transporter gene expression (Fig. 11D). The AP2/ERF gene decreased in the G53 genotype except for the As stress and increased in the G68 genotype except for the Cd and Pb stresses (Fig. 11E). RING finger gene expression decreased in the G53 genotype except for As stress, while it increased in all heavy metal stresses in the G68 genotype (Fig. 11F). Similar expression profile results were seen in the MSL gene (Fig. 11G). In the NRT gene, it decreased in the G53 genotype except for As stress, while it increased in the G68 genotype in all heavy metal stresses except As stress (Fig. 11H).

Fig. 11.

RT-qPCR analysis of Pvul-miR172 gene family members targets genes under heavy metal stresses in G53 and G68 genotypes. (A) ABC transporter (B) AP2 (C) UBC (D) UAA transporter (E) AP2/ERF (F) RING finger (G) MSL (H) NRT

Pvul-miR172 target genes exhibited opposite expression profiles in the two genotypes at some heavy metal stresses. In As stress, a similar expression profile was created in both genotypes, except for the ABC transporter and NRT genes. Both genes exhibited an increased expression level in the G53 genotype and a decreased expression level in the G68 genotype. In Cd stress, different expression responses occurred in two genotypes except for the ABC transporter, AP2, and AP2/ERF genes. As a result of Cd treatment, the expression of other target genes decreased in the G53 genotype and increased in the G68 genotype. As a result of Ni stress, genes other than the ABC transporter and AP2 genes produced opposite expression profiles in the two genotypes. As a result of Ni treatment, the expression of other target genes decreased in the G53 genotype and increased in the G68 genotype. In Pb stress, different expression profiles were detected in both genotypes, except for the ABC transporter, AP2, and AP2/ERF genes. As a result of Pb treatment, the expression of other target genes in the G53 genotype decreased, while the expression of these genes in the G68 genotype increased.

Correlation matrix and Principal Component Analysis (PCA)

A correlation analysis was performed and visualized in graphical form to find the relationship between members of the Pvul-miR172 gene family and their target genes (Fig. 12). In the graph obtained, between Pvul-miR172a and Pvul-miR172d, ABC transporter, AP2, AP2/ERF, and NRT genes; between Pvul-miR172d and Pvul-miR172a, UAA transporter, AP2/ERF, and NRT genes; a positive correlation was found between Pvul-miR172f and UBC, AP2/ERF, and MSL genes. In addition, a negative correlation was found between Pvul-miR172a and UBC, UAA transporter, RING finger, and MSL genes, and between Pvul-miR172c and all target genes, while a positive correlation was found between Pvul-miR172d and all target genes. In addition, between the ABC transporter and the AP2, AP2/ERF and NRT genes, which are among the Pvul-miR172 target genes; between AP2 and ABC transporter, AP2/ERF and NRT genes; between UBC and UAA transporter, RING finger and MSL genes; between the UAA transporter and the UBC, RING finger, and MSL genes; between AP2/ERF and ABC transporter, AP2 and NRT genes; between the RING finger and the UBC, UAA transporter and MSL genes; between MSL and UBC, UAA transporter and RING finger genes; a positive correlation was also found between NRT and ABC transporter, AP2 and AP2/ERF genes.

Fig. 12.

Analysis of the correlation matrix between the members of the Pvul-miR172 gene family and target genes. Pvul-miR172a and Pvul-miR172c showed a negative correlation with their target genes, while Pvul-miR172d and Pvul-miR172f genes exhibited a positive correlation. Furthermore, a significant positive correlation was observed between Pvul-miR172a and Pvul-miR172d

PCA was performed to understand the physiological and biochemical parameters that change due to heavy metal stress. As a result of this analysis, two basic components were identified that collectively explained 88.39% of the total variance and had eigenvalues greater than 1 (Table 4). Variables such as Pro (-0.340), POD (-0.317), and H₂O₂ (-0.268) showed a negative charge on the first fundamental component, PC1, with a variance of 68.97%, and made valuable contributions to the formation of variance. Similarly, the components CMD (-0.266) and SOD (-0.242) also contributed significantly to the negative variance. On the other hand, components such as RWC (0.362) and RL (0.355) exhibited a positive variance. In the PC2 component, which had a variance of 19.42%, the other fundamental component, CMD (-0.431), exhibited a strong negative charge. On the other hand, components such as MDA (0.452), APX (0.422), and H₂O₂ (0.420) exhibited substantial positive charges and contributed to PC2 variance.

Table 4.

Factor loadings of parameters based on PCA. PC1 and PC2 summarize the variance among the variables. PC1 explains 68.97% of the total variance, while PC2 explains 19.42%

Variable	PC1	PC2
H2O2	-0.268	0.420
MDA	-0.199	0.452
SOD	-0.242	0.156
POD	-0.317	0.330
APX	0.260	0.422
Prolin	-0.340	0.137
CC	0.329	0.247
RL	0.355	0.133
SL	0.331	0.154
RWC	0.362	-0.035
CMD	-0.266	-0.431
Eigen Value	7.587	2.136
Percent of variance (%)	68.97	19.42
Cumulative variance	68.97	88.390

APX ascorbate peroxidase, CC chlorophyll content, CMD cell membrane damage, H₂O₂ hydrogen peroxide, MDA malondialdehyde, POD peroxidase, RL root length, RWC relative water content, SL shoot length, SOD superoxide dismutase

A biplot was created for the relationship between heavy metal applications and biochemical and physiological parameters (Fig. 13). This graph indicates that Cd and Pb exhibited a strong negative correlation, whereas the control showed a strong positive correlation with PC1. However, Pb is strongly positively correlated with PC2. The control is not strongly positively correlated with PC2. Ni showed a weak negative correlation relationship with both PC1 and PC2. It has a strong negative correlation for PC2 but not a strong positive correlation with PC1. In the biplot graph, the remote positioning of control and heavy metal applications represents a big difference.

Fig. 13.

Biplot plot results from PCA for heavy metal applications, biochemical, and physiological parameters. As: arsenic, Cd: cadmium, Ni: nickel, Pb: lead, APX: ascorbate peroxidase, CC: chlorophyll content, CMD: cell membrane damage, H₂O₂: hydrogen peroxide, MDA: malondialdehyde, POD: peroxidase, RL: root length, RWC: relative water content, SL: shoot length, SOD: superoxide dismutase

Discussion

Many miRNAs have been identified in plants due to their roles in vital processes and responses to changing environmental conditions [70]. However, the need to identify miRNAs in various plants and to conduct genome-wide characterization of miRNA families has become evident [71, 72]. In this study, six members of the miR172 gene family were identified in P. vulgaris L. (Table 1). Previous studies have identified five members in A. thaliana [73–75], four members in O. sativa [76, 77], three members in Hordeum vulgare [78], and 12 members in G. max [79]. miRNAs originate from hairpin RNA precursors, and hairpin structures are expected to be observed in miRNA structural predictions [80, 81]. Similar to the hairpin structures identified in H. vulgare miR172 precursor sequences, such structures were also observed in members of the Pvul-mir172 gene family (Fig. 2) [82].

Mature miRNAs are single-stranded RNA sequences with lengths ranging from 19 to 24 nt and are evolutionarily highly conserved across different species [83]. A 19 nt segment of the Triticum urartu miR172 mature sequence was similar to mature miRNA sequences in four species [84]. Similarly, the 19 nt segment of the Pvul-miR172 mature sequences was also conserved (Fig. 3).

Phylogenetic analysis conducted on the precursor sequences of miR172 gene family members across eight different organisms revealed that the members were grouped into three distinct classes, consistent with our phylogenetic analysis. Unlike our findings, Ath-miR172c and Zma-miR172a were found to be neighboring genes. The Ath-miR172 gene family members, except for Class 1, were distributed among other classes [85].

Numerous transporters are involved in the uptake of heavy metals from roots to their transfer into cells and tissues, and these transporters are crucial for heavy metal stress tolerance [86]. GO analysis of target genes revealed transport processes as the most abundant biological processes. Furthermore, when examining the molecular functions of target genes, the most common DNA-binding and ATP-binding functions are among the most common stress-response-inducing miRNA targets [87].

Transcription factors and gene expression regulation can be controlled by cis-acting elements [88]. Our cis-acting element analysis (Fig. 5) identified light-responsive G-box and Box 4 elements, hormone-responsive ABRE, ERE, and P-box elements, and stress-related MBS, LTR, and TC-rich repeat elements, which were similarly detected in members of the Brassica napus miR172 gene family [89]. Heavy metal ions first contact plant roots and are transported from the roots to the shoots via the xylem [12, 90, 91]. Therefore, we examined shoot and root lengths—critical parameters affected by heavy metal exposure—under four different heavy metals in two genotypes. The results showed that Cd treatment significantly reduced the lengths in both genotypes. A study investigating the root and shoot lengths of P. vulgaris L. exposed to Cd and Pb stress found that Cd treatment resulted in shorter root and shoot lengths than Pb treatment [92]. In our analyses, shoot length decreased significantly under Ni treatment in both genotypes. For root length, Pb stress resulted in longer roots in the G53 genotype, whereas As stress led to longer roots in the G68 genotype compared to other heavy metals (Fig. 7A- B).

Heavy metal ions disrupt plasma membrane integrity by binding to proteins and lipids in the plasma membrane, thereby causing an imbalance in ionic homeostasis [93, 94]. Our analysis revealed an increase in CMD across all heavy metals. The highest damage occurred under Cd stress in both genotypes, while the least damage was observed under Pb stress (Fig. 8A).

Additionally, heavy metals adversely affect water uptake in plants, reducing RWC. This disruption in water transport processes from the roots leads to damage in plant organs [95, 96]. Our analysis showed that all heavy metals significantly reduced RWC (Fig. 8B). Similarly, another study observed a reduction in RWC in P. vulgaris L. under As and Cd stress [97, 98].

CC indicates plant health, and environmental pollutants, such as heavy metals, accumulate in plant organs, reducing CC [99, 100]. In our analysis, all experimental groups showed reduced CC under heavy metal treatment, with the highest reduction observed under Cd treatment in both genotypes (Fig. 8C). Similarly, Cd and Pb stress decreased CC in P. vulgaris L. in another study [101].

Antioxidant enzymes play a vital role in slowing the oxidation of biomolecules, thereby preventing oxidative chain reactions. Their expression is crucial in mitigating oxidative damage caused by heavy metal stress [102, 103]. Under Cd, Ni, and Pb stress, P. vulgaris L. showed increased levels of APX, POD, and SOD antioxidant enzymes. In studies investigating their effects at varying concentrations, Pb stress generally caused greater increases compared to Cd and Ni stress [14]. Our analyses showed that the G53 genotype treated with Pb similarly resulted in an increase in all antioxidant enzymes. In the G68 genotype, Pb stress caused the greatest increase in POD enzyme activity, while As stress resulted in higher SOD activity compared to Pb (Fig. 9A-C).

Pro, an amino acid, regulates critical functions such as osmotic balance, protein protection, and scavenging reactive oxygen species (ROS), thus contributing to plant stress tolerance [104–106]. Compared to the control group, increased Pro content was observed in P. vulgaris L. under Ni and Pb treatments. Ni stress caused a greater increase in Pro content than Pb stress [107]. In our analysis, a similar result was observed in the G68 genotype, while Pb treatment resulted in a greater increase in Pro content in the G53 genotype (Fig. 9D).

H₂O₂ acts as a signaling molecule at low concentrations, contributing to stress tolerance; however, its excessive accumulation leads to oxidative stress [108–110]. Our analysis revealed increased H₂O₂ levels across all experimental groups except under As stress in the G68 genotype (Fig. 9E). Similarly, increased H₂O₂ levels were observed in P. vulgaris L. exposed to As and Cd treatments in other studies [111, 112].

ROS in stressed plants Pb to membrane lipid peroxidation, increased MDA content, a stress indicator [113, 114]. In our analysis, MDA levels increased in the G68 genotype compared to the control, whereas MDA levels decreased in the G53 genotype except under Pb stress (Fig. 9F). Other studies also observed increased MDA levels in P. vulgaris L. under As, Cd, Ni, and Pb treatments [92, 107, 115].

The expression of miRNAs and their target genes varies dynamically across tissues, depending on time and dose [116]. Exposure to As stress in B. juncea roots revealed decreased Ath-miR172a expression during the first hour, followed by increased expression at 4 h [34]. Similarly, O. sativa roots under As stress showed increased miR172 expression at 6 h, which declined by 24 h [117]. In Helianthus annuus, miR172 expression increased in roots under Cd stress at 1.5 h but remained similar to the control in leaves. At three and 6 h, miR172 expression levels decreased [37]. Under Pb stress, Gossypium hirsutum leaf miR172 expression decreased at 50 µM Pb but increased at 100 and 200 µM concentrations [40]. Our study detected varying expression profiles of Pvul-miR172 between two common bean genotypes exposed to 50 mM doses of four different heavy metals for one week (Fig. 10). These expression differences are thought to be exhibited in resistant and susceptible genotypes to cope with heavy metal stress. Except for Pvul-miR172c under As stress and Pvul-miR172a under Cd stress, all other Pvul-miR172 members exhibited contrasting expression patterns between the two genotypes.

During heavy metal stress exposure, the Pvul-miR172 gene family can respond to stress through their target genes and themselves. One of these target genes, the ABC transporter gene, is responsible for the uptake and transport of metals. Among these transporters, those found in the vacuole play a crucial role in tolerance to heavy metal stress by facilitating metal detoxification [118]. Overexpression of the ABC transporter gene in Populus tomentosa transgenic A. thaliana lines reduced Cd accumulation and enhanced stress tolerance [119]. Similarly, in M. sativa, Pb stress induced higher ABC transporter expression in tolerant varieties compared to sensitive ones [120]. In our study, ABC transporter gene expression showed low levels in both genotypes, except for the G53 genotype under As stress (Fig. 11A). In Vitis vinifera, the expression of ABC transporter genes increased after 2 h of Hg treatment and was downregulated after 12 h [121]. In P. alba, PaABCC13 gene expression decreased in roots on days 7 and 60 after Cd treatment compared to day 1. Similar reduced expression was observed in leaves [122]. Considering these results, the expression of ABC transporter genes in plants exposed to heavy metal stress for 7 days in our study may have decreased due to the accumulation of heavy metals. Our analysis showed that Pvul-miR172d showed a high expression profile in both genotypes under Ni (3-fold in G-53 and 8.8-fold in G-68) and Pb (1.3-fold in G-53 and 2.7-fold in G-68) stresses, unlike its target gene, the ABC transporter (Fig. 10C). It is predicted that Pvul-miR172d mediates the suppression of ABC transporter gene expression in response to these stresses.

Another target gene, AP2 and AP2/ERF transcription factors, is involved in abiotic stress responses through various hormone-dependent and hormone-independent signaling pathways [123]. IIAP2, a member of the AP2/ERF gene family in Iris lacteal, is thought to interact with the metallothionein gene IIMT2a and activate related pathways to reduce Cd toxicity [124]. In Solanum torvum roots under Cd stress, Sto-miR172 expression decreased, whereas its target gene AP2 expression increased [125]. In G. hirsutum leaf tissue under Pb stress, miR172 expression was downregulated at a 50 µM Pb concentration, but upregulated at 100 and 200 µM concentrations. Its expression pattern was inversely correlated with its target gene, AP2 [40]. Under As stress in the G68 genotype, the AP2 gene, a known target of Pvul-miR172a and Pvul-miR172d, was upregulated by 2.4-fold, while the expression levels of Pvul-miR172a and Pvul-miR172d were downregulated, indicating an inverse regulatory relationship (Fig. 11B and E). Similarly, under As stress in the G68 genotype, the AP2/ERF gene targeted by Pvul-miR172a was upregulated by 2.4-fold, whereas Pvul-miR172a was downregulated, indicating an inverse regulatory relationship between them (Fig. 11E). It is thought that Pvul-miR172s in these application groups do not suppress the AP2 and AP2/ERF genes, causing their expression to increase.

One of the key systems responsible for preventing the accumulation of damaged proteins in response to heavy metal stress is the ubiquitin-proteasome system (UPS), which primarily regulates proteolysis within the cell [126]. E2 (UBC), one of the most crucial enzymes in this system, serves as a bridge between the other two enzymes (E1 and E3) and plays a key role in maintaining protein homeostasis [127, 128]. Overexpression of the UBC gene in Nicotiana tabacum lines reduced Cd accumulation and enhanced stress tolerance [129]. In Poa pratensis under Cd stress, UBC gene expression was upregulated in stress-tolerant genotypes [130]. The increased expression of the UBC gene Pb treatment results in an increase in the activity of antioxidant enzymes, suggesting that it is involved in the response to abiotic stress [129, 131]. Our results showed that Pvul-miR172c showed a low expression profile in the G68 genotype under As (1.3-fold), Ni (1.2-fold), and Pb (1.5-fold) stresses, unlike its target gene UBC (Fig. 10B). It is thought that Pvul-miR172c does not suppress the UBC gene, resulting in its increased expression. In addition, the UBC gene exhibits high expression levels in response to all heavy metal stresses in the resistant G68 genotype, suggesting that it may be a gene contributing to the heavy metal tolerance of this genotype (Fig. 11C).

The SLC35 (UAA transporter) family, a member of the solute carrier (SLC) superfamily, transports substrates such as ions, sugars, amino acids, and nucleotides, and is therefore known as a nucleotide sugar transporter (NST). This family transports nucleotide sugars for glycosylation processes in the endoplasmic reticulum (ER) and Golgi apparatus [132]. The UAA transporter gene, an important gene for maintaining the balance in these critical processes, was found to have a low expression profile in the G53 genotype, except for As stress. In contrast, it had a high expression profile in the G68 genotype (Fig. 11D). In O. sativa under Cd stress, UAA transporter expression was upregulated in tolerant varieties [133]. Our analyses showed that, unlike the UAA transporter gene, which is the target gene of all Pvul-miR172s, Pvul-miR172a showed a low expression profile under As (1.6-fold) and Pb (2.2-fold) stresses, Pvul-miR172c under As (1.3-fold), Ni (1.2-fold) and Pb (1.5-fold) stresses, Pvul-miR172d under As (1.4-fold) and Cd (2.2-fold) stresses, and Pvul-miR172f under Pb (2-fold) stress in the G68 genotype, and as a result, they could not suppress the expression of the UAA transporter gene (Fig. 10A-D).

Another gene responsible for degrading damaged proteins after damage due to heavy metal stress is the RING finger gene, which acts as an E3 ligase [134]. Overexpression of RING finger genes in transgenic A. thaliana lines conferred sensitivity under As stress [135]. Similarly, in S. lycopersicum under Cd stress, overexpression of RING finger genes enhanced tolerance [136]. RING finger 1 contributes to stress tolerance by acting as an E3 ligase and regulating proteolysis in A. thaliana under Al stress [137, 138]. The RING finger gene exhibited a low expression profile in the G53 genotype, except for As (1.2-fold) stress, whereas it showed a high expression profile in all heavy metal stresses in the G68 genotype (Fig. 11F). Upon examining our analyses, it was observed that Pvul-miR172c, unlike its target gene RING finger, could not suppress the gene, as evidenced by a low expression profile in the G68 genotype under As (1.3-fold), Ni (1.2-fold), and Pb (1.5-fold) stresses (Fig. 10B).

One of the genes involved in the detoxification of ROS is the MSL gene [139]. In A. thaliana knockout mutants under Cd stress, MSL was crucial for maintaining mitochondrial redox balance [140]. MSL gene expression was low in the G53 genotype, except for As (1.1-fold) stress, while it showed a high expression level in the G68 genotype (Fig. 11G). According to our analysis, Pvul-miR172f exhibited a low expression profile in the G68 genotype under Pb stress (2-fold), unlike its target gene MSL (Fig. 10D).

Heavy metals inhibit plants’ ability to uptake nitrogen, and plants respond using their NRT genes to balance nitrate uptake [141]. Transgenic A. thaliana lines lacking NRT genes showed higher Pb accumulation, highlighting their importance in stress tolerance [142]. While NRT gene expression is at a low level in the G53 genotype, except for As stress (4.5-fold), it is at a high level in the G68 genotype, except for As stress (Fig. 11H). According to our results, it was observed that Pvul-miR172f, unlike its target gene NRT, prevented the suppression of NRT gene expression by exhibiting a low expression profile in the G68 genotype under Pb stress (2-fold) (Fig. 10D).

Our findings suggest that members of the Pvul-miR172 gene family may contribute to tolerance in response to heavy metal stress by targeting specific genes. Previous studies have shown that the increased expression of Pvul-miR172 target genes associated with heavy metal tolerance in the G68 genotype, compared to the G53 genotype, aligns with the G68 genotype’s superior performance in most physiological and biochemical tests. In contrast to these target genes, the Pvul-miR172s displaying a decreased expression profile are hypothesized to play a role in this process via a mechanism that does not involve suppressing their target genes.

Additionally, the low expression levels of the AP2 gene, known to regulate flowering, under stress conditions other than As stress, suggest that Pvul-miR172 may modulate stress responses through an alternative pathway involving repression. These results highlight the complexity of drawing a unified conclusion for all four heavy metals. The variable expression patterns of both Pvul-miR172s and their target genes, depending on the type of heavy metal, serve as a prime example of this complexity.

Therefore, as demonstrated in our study, insights derived from multiple stress conditions are crucial for understanding the roles and functions of miRNAs in stress responses. Such comprehensive knowledge is essential for a deeper understanding of miRNA-mediated stress tolerance mechanisms.

Transgenic approaches utilizing miRNAs that interact with genes involved in plant growth, development, defense, and stress responses enhance abiotic stress tolerance and improve crop yield [22]. Tools such as suppression or overexpression of target genes through artificial miRNAs (amiRNA) and target mimics (STTM) are widely used in the development of such transgenic plants [143, 144]. Furthermore, overexpression of miRNAs can regulate the expression levels of target genes [145]. According to a new approach, miRNA expression can be increased by using miRNA-encoding peptides (miPEPs) [146]. The results obtained from this study provide a basis for the future use of these approaches in developing P. vulgaris L. lines or cultivars resistant to heavy metal stress through miRNA and their targets.

Conclusion

In this study, six members of the miR172 gene family were identified in P. vulgaris L. Their roles in heavy metal stress were investigated. Heavy metals, such as As, Cd, Ni, and Pb, were observed to negatively affect physiological and biochemical parameters, including root and shoot lengths, CMD, RWC, and CC, in two different P. vulgaris L. genotypes. Additionally, increased antioxidant enzyme activity, Pro content, H₂O₂, and MDA levels were observed under heavy metal stress.

The expression profiles of members of the Pvul-miR172 gene family and their target genes were analyzed, revealing that these genes exhibited differential responses to heavy metal stress. These responses also varied between the two different genotypes used in the study. The relationship between the Pvul-miR172 gene family and target genes, particularly in the G68 genotype, may shed light on one of the causes of heavy metal tolerance. These findings suggest that the Pvul-miR172 genes, through target genes, may play a role in developing tolerance to heavy metal stress in P. vulgaris L.

Abbreviations

ABC

ATP-binding cassette

AP2

APETALA2

AP2/ERF

APETALA2/Ethylene-responsive factor

APX

Ascorbate peroxidase

CC

Chlorophyll content

CMD

Cell membrane damage

COX5B-2

Cytochrome c oxidase subunit 5B-2

ERE

Ethylene-responsive element

GO

Gene Ontology

H₂O₂

Hydrogen peroxide

MDA

Malondialdehyde

MEGA

Molecular Evolutionary Genetics Analysis

miRNA

MicroRNA

miPEP

miRNA-encoded peptide

MSL

Mechanosensitive ion channel

MYB

Myeloblastosis (transcription factor family)

NRT

Nitrate transporter

PCA

Principal Component Analysis

POD

Peroxidase

RING

Really Interesting New Gene (a protein domain)

SOD

Superoxide dismutase

SPAD

Soil Plant Analysis Development (chlorophyll meter)

STTM

Short tandem target mimic

Authors’ contributions

Research concept and design: Eİ, BMÖ, MA, and EY; Performed experiments: BMÖ, SU, EY, and EY; Data analysis: BMÖ, MA, AÇ, and AGK; visualization: BMÖ, SM, AÇ, and MA; Preparation of the draft manuscript: ASA, BMÖ, AÇ, and Eİ; Review and editing: Eİ, ASA, and AÇ. All authors have reviewed and approved the published version of the manuscript.

Funding

This study was supported by Erzurum Technical University Scientific Research Projects, Grant Number 2023/39.

Data availability

All RNA-seq data were obtained from the NCBI SRA database with the BioProject ID: PRJNA868129 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA868129/).

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.