Abstract
Micronutrient malnutrition is a global health concern, highlighting the importance of essential micronutrients like zinc in human diet, and enhancing zinc concentration in staple crops like common beans is a promising strategy. In this study, we performed genome-wide association studies to identify genetic loci associated with Zn accumulation in a diverse panel of 177 Turkish common bean landraces and six commercial varieties. Field trials were conducted across two locations over two years to assess Zn concentration variation. The results revealed significant genetic variability, with Zn concentrations ranging from 11.6 to 105.3 mg/kg. Two significant marker-trait associations (MTAs) were identified on chromosomes Pv06 and Pv08, with loci linked to Zn translocation and brassinosteroid-mediated cell-wall remodeling. Candidate genes, Vacuolar Iron Transporter 1 (VIT1) and Wall-Associated Kinase-Like 4 (WAKL4), were implicated in Zn homeostasis and distribution. In silico transcriptomics analysis further validated the role of these genes under Zn-deficient conditions in Arabidopsis. Protein-protein interaction (PPI) network analysis identified key regulatory pathways, including ZIP transporters, heavy metal ATPases, and cell-wall modification enzymes. These findings provide valuable genetic insights for biofortification strategies, facilitating the development of Zn-enriched common bean cultivars to enhance nutritional security. The study underscores the potential of GWAS in identifying stable genetic markers for Zn biofortification, paving the way for future breeding efforts aimed at improving dietary Zn intake through staple crops.
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Introduction
Common beans (Phaseolus vulgaris L.) are particularly important as a staple food in many regions worldwide1. Globally, in 2020, it covered approximately 34 million hectares, yielding 27.5 million metric tons2. This crop holds significant dietary value in Latin America, Eastern, and Southern Africa, where it serves as a primary protein source3. Historical records indicate that common beans were first domesticated in Mesoamerica and later spread to Europe through the Columbian Exchange4,5. The introduction of the common bean to Türkiye dates back to the Ottoman Empire, facilitated by trade routes linking Europe and Asia. The diverse climates and regions within Türkiye have supported a broad spectrum of common bean landraces, providing a rich genetic resource pool for breeders6,7. Over years of cultivation and natural mutations, these landraces have developed a wide array of traits, including diverse morphological and agronomic characteristics and resilience to various biotic and abiotic stresses8.
Zinc (Zn) is essential for both plants and humans, yet its role in common beans remains underexplored. In plants, Zn is vital for physiological processes such as enzyme activation, carbon metabolism, and nucleic acid synthesis9,10. It acts as a co-factor for various enzymes, significantly impacting crop yield and quality. Zn deficiency can lead to reduced productivity and nutritional quality11,12. For humans, Zn is crucial for immune function, protein synthesis, DNA synthesis, wound healing, and cell division13,14. Deficiency in human diets can cause growth retardation, immune dysfunction, and increased susceptibility to infections15,16. Therefore, biofortification of staple crops like common beans is a vital strategy to alleviate global Zn deficiency.
Efforts to improve micronutrient concentration in staple crops have been driven by initiatives like HarvestPlus, launched by CGIAR in 2003, which aims to alleviate global micronutrient deficiencies through biofortification17. Among the essential micronutrients, biofortification of Zn has gained attention due to its cost-effectiveness and sustainability. Unlike post-harvest fortification, biofortification integrates the nutrient directly into the edible portion of the crop, eliminating the need for additional processing. Crops targeted for Zn biofortification include wheat, rice, maize, pearl millet, beans, and bananas, with notable progress in wheat and rice biofortification17.
Common bean genotypes exhibit considerable variability in Zn concentrations, offering potential for nutrient enhancement through genetic improvement18,19,20,21. However, environmental factors significantly influence the expression of Zn concentration, making it essential to identify stable genetic markers for trait selection22,23,24. Linkage mapping studies have identified several quantitative trait loci (QTLs) associated with Zn in bi-parental populations25,26,27,28,29but the limited scope of linkage mapping hinders its applicability to broader genetic pools. In contrast, genome-wide association studies (GWAS) offer a more comprehensive approach, allowing the identification of trait-associated loci across diverse populations.
The advent of next-generation sequencing (NGS) technologies has revolutionized genetic analysis, enabling the cost-effective use of genotyping-by-sequencing (GBS) for large-genome species30. GWAS have since become a valuable tool to identify QTLs linked to essential micronutrients across legume species, including chickpea31,32,33lentil34,35and field pea36. For common beans, Mahajan et al.37 identified 13 simple sequence repeats (SSRs) linked to Fe, Zn, and protein concentrations, with Zn-associated markers located on chromosomes 1, 3, 5, 7, and 10. However, their use of the GLM model led to spurious associations between markers and micronutrient concentrations38. Considering the genetic diversity within the two distinct gene pools of common beans (Mesoamerican and Andean), understanding this diversity is crucial for GWAS accuracy. Research on DArTSeq SNP markers in common beans is promising but limited. Studies on 174 Croatian landraces using 26 SSR and 6599 DArTSeq SNP markers identified loci for Fe and Zn on chromosomes 3 and 6, respectively, through single and multi-locus mixed model (MLMM) approaches39. In contrast, Gunjaca et al.40 did not identify any quantitative trait nucleotides (QTNs) associated with grain iron concentration. Conversely, Delfini et al.18 identified seven quantitative trait loci (QTLs) linked to zinc and two associated with iron in their study of 178 Mesoamerican accessions, which deepens the understanding of the genetic factors influencing micronutrient composition in common beans.
Malnutrition remains a critical global challenge, affecting millions of people and leading to severe health issues, particularly in regions dependent on staple crops for their primary nutrition41,42,43. Addressing this problem requires innovative approaches to enhance the nutrient concentration of essential crops. Presently, Türkiye has over 35 registered dried bean varieties and 200 fresh bean varieties, as documented by the Seed Certification and Variety Registration Center (www.tarimorman.gov.tr). Therefore, this study aims to assess the variations in Zn concentration and identify genomic regions associated with Zn accumulation in Turkish common bean germplasm across diverse environments. By employing GWAS, the study seeks to detect established and novel loci linked to Zn concentration, offering valuable insights for biofortification efforts and genetic enhancement of this vital crop.
Materials and methods
Plant material and field experimentation
This study involved 177 common bean landraces and six commercial varieties: Akman, Göynük, Karacaşehir, Önceler, Göksun, and Akdağ. The germplasm landraces used in this study were collected by a group of researchers (Baloch FS and Çiftçi V) from various farmers’ fields across different geographical provinces in Türkiye. This plant material was sourced from 19 different provinces in Türkiye, representing primary bean-cultivating regions, and conserved at Bolu Abant Izzet Baysal University (BAIBU). The collection was conducted in compliance with local agricultural and research regulations and they were obtained with the consent of the farmers. Previous research validated these commercial cultivars as standard references39,44. The detailed information on the plant material is provided in (Supplementary Table S1).
Field trials were conducted in Bolu and Sivas, with experiments spanning two years in each location (2017 and 2018). This approach aligns with established agricultural research practices45. The trials were conducted as an augmented block design, remarked for its accuracy, efficiency, and reliability over alternative experimental setups46. Control cultivars were replicated within each block to standardize data and enable the calculation of adjusted means for subsequent analysis46. Details on sowing, harvesting, and agronomic practices are available in our prior publications47,48.
DNA isolation
DNA extraction followed the CTAB method developed by Doyle and Doyle49 and specific protocols from Diversity Arrays Technology (DArT). Full details on the plant DNA extraction procedure can be found at https://www.diversityarrays.com/orderinstructions/plant-dna-extraction-protocol-for-dart/. The quality of isolated DNA was checked via 0.8% agarose gel electrophoresis, and DNA quantification was performed using the DS-11 FX spectrophotometer/fluorometer. DNA samples of sufficient quality were diluted to a final concentration of 50 ng µL⁻¹ and sent to Diversity Array Technology Pty, Ltd, Australia (http://www.diversityarrays.com/) for DArTseq genotyping.
Genotyping-by-sequencing (GBS) analysis for DArTseq markers
DArTseq incorporates a complexity reduction strategy coupled with next generation sequencing30,50 to target genomic regions associated with significant plant traits51. The method was optimized for common beans by selecting suitable genome fractions and representation size, using PstI-MseI enzymes for complexity reduction. The digestion/ligation reactions were conducted as per Kilian et al.50. PCR amplification of PstI–MseI fragments was performed for 30 cycles under the following conditions: initial denaturation at 94 °C for 1 min, followed by 29 cycles of 94 °C for 20 s, a ramp to 58 °C at 2.4 °C/s, 58 °C for 30 s, another ramp to 72 °C at 2.4 °C/s, 72 °C for 45 s, and a final extension at 72 °C for 7 min, then held at 10 °C. Amplified products from the 96-well plates were pooled in equimolar concentrations, loaded onto c-Bot (Illumina) for bridge PCR, and sequenced using the Illumina HiSeq2000 platform with 77 single-read cycles. Sequence data were processed with DArT’s proprietary analysis pipelines51and poor-quality sequences were filtered out, particularly in barcode regions, to ensure accurate sample assignment during the barcode split. Approximately 4,000,000 sequences per barcode/sample were analyzed for marker calling, with identical sequences condensed into “fastqcall” files for use in downstream SNP and SilicoDArT marker identification using DArTsoft14.
The resulting silicoDArT markers are genetically dominant and were scored in a binary fashion, with 1 and 0 representing the presence and absence, respectively, of a restriction fragment in the genomic representation of each sample. They were screened based on parameters such as call rate, polymorphism information concentration (PIC), and reproducibility. Markers with PIC, reproducibility, and call rate values lower than 0.1, 1.0, and 0.9, respectively, were excluded from the bioinformatics analysis to avoid false inferences.
Determination of Zn concentration in beans
Zn concentration in each bean accession was measured. Seed samples were rinsed twice with deionized water and oven-dried at 65 °C. The dried samples were ground into a fine powder using specialized mills (Z341789 Aldrich, Merck, Germany). For digestion, 0.2 g of the seed powder was treated with 5 mL of concentrated nitric acid (65%) and 2 mL of hydrogen peroxide (35%) in a microwave digestion system (ETHOS EASY, Milestone, Italy)52. The digested samples were diluted to a final volume of 20.0 mL with ultra-pure water and analyzed for Zn concentration using an Atomic Absorption Spectrophotometer at a wavelength of 213.9 nm53.
Marker-associated loci identification and gene ontology (GO) analysis
To identify the marker-associated loci, 100 kb up and downstream of the SNPs were investigated in common bean genome browser at legume information system (LIS: https://legumeinfo.org/)54,55,56 and at Phytozome V.13 (http://phytozome.jgi.doe.gov/pz/portal.html)57. Since common bean genes have not been characterized yet, their Arabidopsis orthologs were searched against the Arabidopsis thaliana genome (TAIR 10) via TAIR BLAST 2.9.0 +58, and the loci were selected with the highest hit score and E cut-off value of 0.001. Next, gene ontologies (GO) of Arabidopsis orthologs were investigated at TAIR 10 and the genes related to Zn ontology were determined. Two putative genes, one for each SNPs, were identified as associated with Zn ontologies.
In silico transcriptomics analysis of Zn deficiency in arabidopsis
Two analyses were performed by using publicly available Zn deficiency transcriptomics data in Arabidopsis. To this end, first the microarray datasets GSE11144359 and GSE7728660 were downloaded from Gene Expression Omnibus61 and were analyzed by GEO2R tool62 with log2 fold change ≥ 2 and significance level P-value of 0.05 after quantile normalization to the expression data. Adjustment to the P-values were done by Benjamini & Hochberg (False discovery rate - FDR) analysis63. Sinclair et al.59 applied Zn deficiency to 7 weeks old wild-type Colombia 0 (Col-0) Arabidopsis ecotype for three weeks. They also applied Zn deficiency for 21 days to hma2hma4 double mutants, which shows repression of locally regulated Zn deficiency responses in the roots whereas shoot-governed systemically regulated Zn deficiency responses of roots are enhanced. Azevedo et al.60 applied Zn deficiency to 3 weeks old wild-type Colombia 0 (Col-0) Arabidopsis ecotype for seven days. They also applied Zn deficiency to bzip19bzip23 double mutants, with knock out of essential regulators of the response to zinc deficiency64. At the end of these analyses, differentially expressed genes (DEGs) were determined in each microarray experiment.
In the second analysis, the expression levels of genes encoding for the proteins in each protein-protein interaction (PPI) network were investigated in the microarray datasets GSE11144359 and GSE7728660with the same analysis parameters. Then, a heat map of the expression patterns was generated by “plotty” function in ggplot2 package in R following Euclidean distance hierarchical clustering65.
Protein-protein interaction (PPI) network, clustering and enrichment analysis
DEGs identified in transcriptomics analysis of GSE11144359 and GSE7728660 and two putative genes identified as associated with Zn ontologies were used to search for the PPI networks through String version 12.066 with the minimum required interaction score of 0.9. To enhance the reliability and quality of the PPI networks, we applied a filtering process using the ‘combined score’ with a recommended threshold67 of 400. Then, the proteins in the network were clustered by k-means in three clusters68. Finally, the genes in these clusters were used as a set to determine the gene ontology (GO) enrichment for molecular function and biological process by using Fisher’s exact test with the Bonferroni correction for multiple testing67 (p < 0.01) and visualized by String version 12.0 with a signal and a strength rate of ≥ 0.01.
Statistical analysis
Statistical analysis were obtained using an online software tool for augmented block design, as developed by Rathore et al.69. The analysis followed a two-step approach. Initially, an analysis of variance (ANOVA) was conducted within individual environments to estimate adjusted means. Subsequently, these means were subjected to a combined ANOVA across environments by fitting an appropriate mixed model to simultaneously account for both genotypic and genotype × environment interaction (G×E) effects. Prior to the combined analysis, the Fligner–Killeen test was performed to assess the homogeneity of variances across environments. In the mixed model framework, environmental effects were treated as random, while genotypic effects were considered fixed, following the methodology proposed by Gomez and Gomez45. Heritability was estimated using variance components obtained from a linear mixed model, in which both genotype and environment were treated as random effects, following the approach described by Habyarimana70. The mean, range, and provincial diversity for traits were computed using XLSTAT software (www.xlstat.com). The pooled coefficient of variation (CV) was calculated across both locations using the methodology suggested by Gomez & Gomez45. The CV was calculated to assess the variability in Zn accumulation across the two years. The pooled CV was computed by calculating the pooled standard deviation for each year and then the overall pooled CV across both years. The pooled standard deviation for 2017 and 2018 was calculated separately, followed by the computation of the pooled standard deviation across both years. Finally, the pooled CV for both years was determined. To understand the stability of the Zn concentration variation among entire accessions, Pearson correlation analysis was performed among the environments through ggcorrplot and ggplot2 packages in R 3.4.1 statistical software. The stability of Zn concentration across accessions was assessed with the “STABILITYSOFT” software71 using stability indices such as regression coefficients, coefficient of variation, deviation from regression, and stability variance. A constellation plot was generated in JMP 14.1.0 software (2018, SAS Institute Inc., Cary, NC, USA). Marker-trait associations (MTAs) were analyzed using a mixed linear model (MLM, Q + K) as described in prior studies47,72with the kinship (K) matrix following Bradbury et al.38. TASSEL 5.0.5 software (https://tassel.bitbucket.io) was used for the analysis. The mean or overall values of both years and location were as phenotypic data for the MTA’s analysis. The germplasm population structure was previously determined48with Q-matrix values used accordingly. Bonferroni and FDR thresholds were applied to identify significant DArTseq markers (p ≤ 0.01). The Manhattan plot was created using R 3.4.1 (http://www.r-project.org/) with the qq-man package73 to visualize significant markers linked to Zn concentration. A physical map based on chromosome and physical base pair distance between the SNP markers associated with Zn concentrations was developed through R 3.4.1 statistical software.
Results
Zinc concentration profiling
The results showed a significant variation in Zn concentrations among the studied germplasm (Supplementary Table S2). The maximum Zn concentration in Bolu 2017 was 79.9 mg/kg for (Bitlis-40) and a minimum 13.7 mg/kg for (Van-65), respectively, while the accession Sivas-16 exhibited maximum Zn concentration (43.8 mg/kg ) in the Bolu 2018 and minimum Zn concentration was reflected for Mus-53. In Sivas, the maximum Zn concentration 105.3 and 103.3 were observed for Yalova-20 while the minimum 12.3 mg/kg (Bingol-16) and 11.6 mg/kg (Bingol-60) were found at Sivas 2017 and Sivas 2018 respectively. Combining data from all four environments, the average Zn concentration across the studied germplasm was 35.93 mg/kg (Supplementary Table S2). Among the genotypes, Bingol-58 had the lowest Zn concentration at 20 mg/kg, while Yalova-20 had the highest at 64.60 mg/kg. analysis of variance.
The analysis of variance was performed on the studied germplasm. Heritability was also calculated, and the heritability was found 0.627 (Table 1).
Descriptive statistics of Zn concentrations
The descriptive statistics of maximum, minimum, and average Zn concentrations and standard deviations are presented in (Table 2). The frequency distribution graph was made for the visualization of the distribution of Zn concentrations among the studied germplasm (Fig. 1). Understanding zinc concentration at the provincial level is crucial for identifying regions with favorable conditions for higher Zn concentration, guiding breeding efforts, and improving soil and crop management practices. Therefore, we have assessed the Zn concentrations at the province’s base and presented them in (Table 3). The maximum mean Zn concentrations were observed in the Yalova province, and the minimum was found in Bursa (Table 3). Stability analysis was also performed, and a total of 10 most stable accessions were investigated (Table 4). Pearson correlation analysis was also performed to exhibit phenotypic plasticity across environments. A highly significant and positive correlation was observed between Sivas 2017 and Sivas 2018 (Fig. 2).
Frequency distribution of the Zn concentration of the studied Turkish common bean germplasm.
Pearson correlation analysis for the studied environment.
The constellation plot effectively categorized the analyzed germplasm into two main groups, A and B (Fig. 3). Group A consisted of 35 accessions, which were further subdivided into two subgroups: A1 and A2. Subgroup A1 included 15 accessions: Sivas-44, Sivas-69, Sivas-70, Bilecik-1, Bilecik-7, Bilecik-10, Balıkesir-4, Balıkesir-5, Düzce-1, Düzce-9, Yalova-13, Yalova-20, Yalova-21, Erzincan-1, and Erzincan-4. Subgroup A2 contained 20 accessions: Bitlis-114, Van-11, Van-65, Elazığ-29, Muş-1, Muş-2, Muş-7, Muş-10, Muş-18, Muş-48, Sivas-7, Sivas-12, Sivas-13, Sivas-16, Sivas-17, Bilecik-2, Balıkesir-3, and Balıkesir-6.
Group B contained the remaining accessions, which were further divided into subgroup B1 and subgroup B2. Subgroup B1 predominantly included accessions such as Bingöl-1, Bingöl-61, Bingöl-65, Hakkâri-55, Bitlis-14, Bitlis-79, Bitlis-90, Bitlis-97, Tunceli-11, Van-27, Elazığ-14, Muş-28, Muş-41, Muş-49, Sivas-18, Balıkesir-19, Akman, Göynük, and Karaceşehir. Subgroup B2 was further divided into two sub-subgroups: B2A and B2B. Subgroup B2A consisted of Bingöl-11, Hakkâri-23, Bitlis-22, Malatya-45, Van-51, Van-59, and Muş-2. Meanwhile, subgroup B2B included Bingöl-16, Bingöl-18, Bingöl-45, Bingöl-61, Bingöl-65, Hakkâri-7, Hakkâri-11, Malatya-32, Tunceli-1, Van-47, Elazığ-10, Elazığ-25, Balıkesir-17, Balıkesir-18, Bursa-1, and Niğde.
Constellation plot for Zn concentrations in Turkish common bean germplasm.
Genetic basis linked with Zn concentrations
During the present investigation, DArT-3,366,319 and DArT-3,372,823 were present on chromosome Pv08 and Pv06 showed a significant association for Zn concentrations (Table 5 and Figs. 4 and 5). The quantile–quantile (Q–Q) plot for the GWAS of grain Zn accumulation in common bean is provided in (Supplementary Fig. S1).
Manhattan plot for Zn concentration in Turkish common bean germplasm.
Physical map of chromosomes and physical base pair distance (bp) displaying the significantly associated markers to Zn concentration.
Identification of as putative genes
Marker 3,366,319 was identified at position 6,325,046 on chromosome 8 with an R2 value of 0.0687 while marker 3,372,823 was localized at position 24,631,330 on chromosome 6 with an R2 value of 0.06504. To determine the genes in MTAs, loci around 100 kb up and downstream SNPs were investigated from the P. vulgaris genome browser (Supplementary Table 3). Accordingly, 27 and 28 loci were identified around marker 3,366,319 and marker 3,372,823, respectively. Since common bean genes have not been characterized yet, A. thaliana orthologs of these genes were identified by BLASTp search at TAIR (Supplementary Table 3), and their gene ontologies were identified, representing that only four genes were related to Zn-associated ontologies (Supplementary Table 4). Among these four genes, two genes, namely FAR1-RELATED SEQUENCE 10 (FRS10) and FRS11, included a molecular function GO of “enables zinc ion binding”, there are no studies proving their involvement in Zn deficiency responses, or Zn homeostasis in plants. Therefore, they were eliminated from further analyses. At the end of this investigation, two putative genes, one for each SNPs, were identified as associated with Zn ontologies (Table 6). The first putative gene VACUOLAR IRON TRANSPORTER 1 (VIT1 - AT2G01770) is primarily known for its function in seed iron accumulation, but its interaction with other divalent metals such as Zn2+ and functions in their translocation has been shown in plants74. The second putative gene WALL ASSOCIATED KINASE-LIKE 4 (WAKL4 - AT1G16150) is a cell wall-associated receptor-like kinase that is required for the up-regulation of zinc transporter genes under Zn deficiency75.
In silico transcriptomics analysis of Zn deficiency in arabidopsis
Next, we analyzed publicly available Zn deficiency transcriptomics data in Arabidopsis. For this purpose, microarray datasets GSE11144359 and GSE7728660 were investigated to determine the DEGs in roots and shoots of wild-type Col-0 under short60 (7 days) and long59 (21 days) Zn deficiency treatment in hydroponics. We identified 45 and 110 DEGs in short and long Zn deficiency treatments, respectively (Supplementary Table 5). To further strengthen the connection between identified DEGs and Zn deficiency responses, we also investigated the DEGs in roots and shoots of bzip19bzip23 double mutants under short Zn deficiency treatment and hma2hma4 double mutants under long Zn deficiency treatment. We identified 61 DEGs in bzip19bzip23 mutants in short Zn deficiency treatments while 36 DEGs in hma2hma4 mutants under long Zn deficiency treatment (Supplementary Table 5). Finally, we identified 4 common DEGs in Col-0, hma2hma4 and bzip19bzip23 under both short and long Zn deficiency treatments (Supplementary Table 5). These analyses represented the core group of DEGs related to Zn deficiency responses in Arabidopsis and include IRON REGULATED TRANSPORTER 3 (IRT3 - AT1G60960), ZINC TRANSPORTER 4 (ZIP4 - AT1G10970), HEAVY METAL ATPASE 2 (HMA2 - AT4G30110) and TOUCH 4 / XYLOGLUCAN ENDOTRANSGLUCOSYLASE/HYDROLASE 22 (TCH4/XTH22 - AT5G57560).
Clustering and expression patterns
To further investigate the relationship between the core group of DEGs related to Zn deficiency responses in Arabidopsis and two putative genes identified as associated with Zn ontologies in our MTAs, we draw the PPI networks through String (Fig. 6). PPI networks of VIT1 and the core group of DEGs and WAKL4 and the core group of DEGs separated into two major networks, each of which included 24 and 32 proteins, respectively. After k-means clustering of these networks, 17 and 15 proteins were grouped in Clusters 1 and 2, respectively, in the PPI of WAKL4 while 7, 4 and 13 proteins were grouped in Clusters 3, 4 and 5, respectively, in the PPI of VIT1 (Table 7). Surprisingly, Cluster 1 included several proteins with diverse functions. However, their association with Zn transport and/or homeostasis has not been studied yet. Cluster 2 included brassinosteroid-related cell expansion proteins, such as EXORDIUM (EXO), SPIRAL1-LIKE5 (SP1L5) and WALL ASSOCIATED KINASE-LIKE 4 (WAKL4) in addition to glutamate receptor proteins and some transcription factors from the families of ERF, WRKY and MYB. Clusters 3, 4 and 5 included proteins that were associated with divalent metal transport.
PPI network of the core group of DEGs, VIT1 and WAKL4. VIT1 and WAKL4 are presented in red while the core group of DEGs were presented in gray.
Since some of these proteins have not been associated with Zn before, we determined the GO enrichment of the proteins in each cluster for molecular function and biological process (Supplementary Table S6). Accordingly, we identified that Cluster 1 enriches in “regulation of nucleotide metabolic process” and “regulation of miRNA metabolic process” while Cluster 2 enriches in “cellular response to amino acid stimulus” and “calcium ion transport” ontologies in WAKL4 PPI network. Interestingly, GO enrichment analysis showed that Cluster 3 enriches in “zinc ion transport”, Cluster 4 enriches in “intracellular zinc ion homeostasis” while Cluster 5 enriches in “siderophore-dependent iron import into cell”, “nicotianamine biosynthetic process” and “vascular transport” ontologies in VIT1 PPI network. Since the genes in Clusters 1 and 2 have not been associated with Zn transport and/or homeostasis, we investigated the GO enrichment of Clusters 1 and 2 (WAKL4 PPI network) and Clusters 3,4 and 5 (VIT1 PPI network) separately (Fig. 7). Accordingly, WAKL4 PPI network was enriched in biological processes like “cellular response to amino acid stimulus”, “response to brassinosteroid” and cellular response to organic substance” while it was enriched in molecular functions of “glutamate receptor activity”, “ligand-gated ion channel activity” and “calcium channel activity” (Fig. 7a). VIT PPI network was enriched in biological processes including “transition metal ion transport”, “nicotianamine biosynthetic process” and “response to zinc ion” while it was enriched in molecular functions of “transition ion transmembrane transporter activity”, “zinc ion transmembrane transporter activity” and “nicotianamine synthase activity” (Fig. 7b). Taken together, GO enrichment analyses represent that the WAKL4 PPI network is primarily associated with responses to organic substances and ion channel activities, whereas the VIT1 PPI network is strongly linked to processes and molecular functions related to zinc ion transport, transition metal homeostasis, and nicotianamine biosynthesis, suggesting distinct yet complementary roles in cellular metal ion regulation.
GO enrichment of WAKL4 and VIT1 PPI networks. (a) Biological process and molecular function ontologies of WAKL4 PPI network. (b) Biological process and molecular function ontologies of VIT1 PPI network.
Finally, we investigated the expression patterns of the genes coding for the proteins identified in PPI networks in Zn deficiency transcriptomics data in Arabidopsis (Fig. 8). Many genes within the WAKL4 PPI network (Clusters 1 and 2) exhibited a similar pattern among themselves, which differed from the distinct trend observed in the VIT1 PPI network (Clusters 3, 4, and 5) in wild-type roots and shoots under Zn deficiency. Some of the genes showed higher expression in the roots, while others were more prominent in the shoots. Additionally, their expression patterns varied depending on the duration of Zn deficiency. Notably, many genes in Clusters 3, 4, and 5 were significantly suppressed in wild-type Col-0, particularly during prolonged Zn deficiency. However, this suppression was reversed in the hma2hma4 and bzip19bzip23 mutants under Zn-deficient conditions, suggesting that the genes in the VIT1 PPI network function as a co-expressed cohort during Zn deficiency.
In silico expression levels of WAKL4 and VIT PPI networks in Zn deficiency and Zn homeostasis-related mutants. Expression levels of WAKL4 and VIT protein networks under zinc deficiency were analyzed in the roots and shoots of wild-type Col-0 after 7 days (GSE77286) and 21 days (GSE111443) in hydroponic conditions. Expression patterns in bzip19bzip23 and hma2hma4 double mutant roots and shoots were also evaluated. A heat map illustrating these expression patterns was generated using the “plotty” function in the ggplot2 package in R, utilizing Euclidean distance hierarchical clustering.
Discussion
Zn is an essential micronutrient for human health, playing a critical role in combating malnutrition, which remains a significant global challenge. Malnutrition, particularly micronutrient deficiencies like zinc deficiency, disproportionately affects populations in developing countries, leading to stunted growth, weakened immunity, and other severe health issues. Common bean, a nutritionally rich legume and a primary protein source for millions worldwide holds immense potential for addressing Zn deficiency through biofortification. However, the productivity and nutrient concentration of common beans are significantly influenced by environmental stresses, including those exacerbated by climate change76,77. As climate change continues to threaten global food security with extreme weather events and declining crop yields, there is an urgent need to identify and develop common bean cultivars that are resilient to such stresses while maintaining or enhancing their Zn concentration. This study aimed to investigate the genetic variability of Zn concentrations and revealed the genetic basis of Zn concentration variation in Turkish common bean germplasm using genome-wide association studies (GWAS). By identifying genomic regions associated with Zn accumulation, our findings contribute to the development of climate-resilient, nutrient-rich common bean cultivars, providing a sustainable solution to mitigate both food insecurity and malnutrition.
Several studies have focused on Zn concentration in common bean seeds. Guzmán-Maldonado et al.78 analyzed wild and weedy common bean genotypes for some essential micronutrients and reported maximum Zn concentration of 33.1 mg/kg. Celmeli et al.79 reported a landrace with Zn concentration of 37.9 mg/kg, with a similar value reported by Brigide et al.80 for five biofortified cultivars (37.7 mg/kg). Pinheiro et al.81 analyzed 155 Portuguese landraces and Blair et al.82 analyzed a wide sample of 365 genotypes (mostly African landraces) for Zn concentration, and they detected slightly higher values (45.3, 49.1 mg/kg, respectively). Philipo et al.83 reported a common bean genotype with Zn concentration of 64.7 mg/kg in an analysis of 99 common bean landraces, cultivars, and breeding lines grown in Tanzania.
The present study explored the Zn concentration across the studied germplasm ranged 20–64.60 mg/kg with an average Zn concentration 35.93 mg/kg and was in line with the study of Jan et al.84 who reported an average Zn concentration of 36.21 mg/kg. However, it is notable that in previous studies, seed Zn concentration in germplasm was found quite low, namely, Zn (17.5–32.3 ppm)85; (25–32 ppm)86; (17.7–42.4 ppm)26; (29.33–65.50)87; (30.90–64.60 ppm)88. Pardes et al.89 reported an average Zn concentration 29 mg kg−1 which was lower than our results. Such differences in seed Zn concentration between this study and earlier studies are attributed to differences among cultivars/genotypes, inclusion of different numbers of genotypes, differences in soil types, and environmental conditions. Despite these germplasm evaluations, understanding the genetic control of Zn is essential to improve the breeding process for these important nutritional traits. Seed Zn concentrations are predominantly quantitative/metric traits90,91whereas previous studies have shown monogenic inheritance for seed zinc92,93,94. Frequency distribution analysis revealed a continuous normal distribution for Zn concentration (Fig. 1). The Zn concentration variability is also assessed on the provincial level because it is crucial for identifying regions with optimal conditions for higher zinc concentration. The maximum mean zinc concentration was observed in the Yalova province, while the minimum mean zinc concentration was found in Bursa (Table 3). The availability of substantial variation for Zn in our germplasm suggests that genotypes with high Zn concentration can be utilized in future biofortification programs, transcriptomics studies, and other genomics studies. As for biofortification programs, higher seed Zn concentration should be greater than 31 ppm according to the classification proposed by Tryphone and Nchimbi-Msolla91. In the current study, we found many genotypes possessing seed Zn concentration > 31 ppm, respectively. These genotypes are well-suited for biofortification programs aimed at enhancing zinc levels in commercial cultivars.
The constellation plot analysis revealed a distinct division of germplasm into Groups A and B with hierarchical subdivisions, suggesting that while some geographic clustering was observed, particularly in Subgroup A1 (northwestern and central Anatolia) and Subgroup A2 (eastern regions), the classification was not strictly geographically exclusive (Fig. 3). The presence of overlapping geographic origins in Group B (e.g., Bingöl, Hakkâri, Bitlis, and Malatya) and the inclusion of diverse locations like Bursa and Niğde in Subgroup B2B indicate that factors beyond geography contributed to the observed grouping. Moreover, the significant variation in Zn concentration within and across groups, with high-Zn accessions appearing in both A and B, further suggests that Zn concentration was not the primary basis for clustering. Instead, genetic divergence, environmental adaptation, or breeding history likely played a more prominent role in structuring the germplasm. The presence of accessions from the same region (e.g., Muş) in multiple subgroups reinforces that neither geographic origin nor Zn concentration alone fully explains the observed genetic structure.
Furthermore, during the present study, DArT-3,366,319 and DArT-3,372,823 were present on chromosome Pv08 and Pv06 showed a significant association for Zn concentrations in Turkish common bean (Fig. 4). A physical map was also developed that revealed narrow regions between the DArTseq markers on Pv06 and Pv08 (Fig. 5). A study conducted using 174 Croatian common bean landraces and DArTSeq SNP markers detected only one loci for Zn concentration on chromosome 6, using single model and MLMM (multi-locus mixed-model)40. This finding aligns with our present research where SNP markers on Pv06 have been linked to zinc accumulation in common bean seeds. Such consistency across multiple studies strengthens the evidence that Pv06 harbors key loci influencing zinc concentration, making it a promising target for marker-assisted breeding and biofortification efforts.
In our PPI network and clustering analyses, we identified 17 proteins in Cluster 1 and 15 in Cluster 2. Notably, the proteins in these clusters have not been previously linked to Zn uptake, translocation, or homeostasis in plants. Conversely, Clusters 3, 4, and 5, comprising 7, 4, and 13 proteins respectively, include several proteins associated with Zn uptake, translocation, and accumulation (Fig. 6). GO enrichment analysis of these clusters revealed Zn-dependent biological processes and molecular functions, such as “cellular response to zinc ion” and “zinc ion transmembrane transport” (Supplementary Table S6). These findings suggest that while Clusters 1 and 2 may involve novel proteins not previously associated with Zn homeostasis, Clusters 3, 4, and 5 contain proteins directly linked to Zn-related processes.
GO enrichment analysis of the proteins in Cluster 1 revealed enrichment in pathways related to “regulation of nucleotide metabolic process” and “regulation of miRNA metabolic process” (Supplementary Table S6). Notably, the association of these pathways with Zn transport and homeostasis has not been extensively studied, presenting an opportunity to explore novel mechanistic insights. Among these proteins, TCH4/XTH22 emerged in the core group of DEGs related to Zn deficiency responses in Arabidopsis (Supplementary Table S4). Studies have shown that TCH4/XTH22, a member of the xyloglucan endotransglucosylase/hydrolase (XTH) family, is involved in cell wall remodeling under nutrient stress conditions, such as boron deficiency95,96. Prior research has shown that XTH22 overexpression leads to cell wall thickening and increased primary root length in Arabidopsis, while xth22 mutants exhibit slower root growth97,98. The cell wall ultrastructure has been observed to thicken in species like Triticum aestivum99 and Paulownia tomentosa100 when grown under excessive Zn conditions, suggesting a protective mechanism against metal-induced toxicity. Additionally, Zn deficiency or excess can alter cell wall composition, as components like cellulose, hemicellulose, and pectins serve as Zn-binding agents within the plant cell wall101. Given that Zn is a crucial micronutrient affecting various physiological processes, including cell wall integrity, our findings suggest that TCH4/XTH22 may play a significant role in modulating cell wall properties in response to Zn availability. This potential function of TCH4/XTH22 in Zn-mediated cell wall modifications could represent a novel mechanism by which plants adapt to fluctuating Zn levels, thereby advancing our understanding of Zn homeostasis beyond existing knowledge. Moreover, since the cell wall plays a role in metal ion binding and transport, alterations in its structure and composition could influence Zn accumulation in seeds, impacting seed Zn concentration and nutritional quality.
In the same cluster, ABL INTERACTOR-LIKE PROTEIN 2 (ABIL2) was identified (Table 7), part of a small protein family of four members involved in regulating actin nucleation and microtubule organization, essential for nutrient transport and plant cellular organization. This suggests ABIL2’s potential influence on cellular organization and plant development under Zn-dependent conditions. Additionally, BRZ-INSENSITIVE-LONG HYPOCOTYLS 2 (BIL2), encodes a mitochondrial DnaJ/Hsp40 family protein that promotes plant growth and stress resistance through brassinosteroid (BR) signaling-mediated protein folding102. The interplay between microtubule organization, cell wall modification, and metal stress responses is crucial for maintaining cellular integrity, as disruptions can impair cellulose deposition and alter plant morphology103,104. Given the known interplay between BR signaling and metal transport, these findings suggest the possibility that BIL2 could influence Zn homeostasis indirectly by modulating stress responses. Furthermore, our analysis identified other DEGs within Cluster 1 that have not been previously linked to Zn homeostasis. Investigating these genes may uncover additional pathways and mechanisms involved in Zn transport and regulation, offering new perspectives on plant nutrient management.
Several proteins in Cluster 2 belong to the glutamate receptor-like (GLR) family (Table 7), which is essential for development, carbon-nitrogen balance, abiotic stress sensing, signaling, and ion homeostasis105. GLRs function as non-selective cation channels, facilitating calcium (Ca2+) influx across the plasma membrane, thereby generating specific Ca2+ signatures in response to various stimuli and mediating long-distance signaling through electrical activity106. While the specific role of GLRs in Zn homeostasis remains to be fully elucidated, their established involvement in Ca2+ signaling suggests a potential indirect influence on Zn physiology in plants. Notably, recent studies indicate that Ca2+ signaling regulates Zn homeostasis by modulating the expression and activity of Zn transporters during deficiency107. GLR-mediated Ca2+ uptake in cells may activate protein kinases in a signaling cascade, which cause Ca2+-dependent protein kinases to phosphorylate Zn transporters, optimizing Zn uptake and distribution108. A similar Ca2+-dependent activation strategy was already established for sodium (Na+) in plants under salinity stress109. Furthermore, Ca2+ signaling plays an important role in modulating root architecture, thereby enhancing a plant’s ability to acquire essential nutrients such as Zn from the soil110. Mutations in GLR genes can disrupt auxin signaling pathways, leading to altered Ca2+ dynamics and subsequent changes in root system architecture. Taken together, our findings suggest that GLRs in Cluster 2 may be involved in Zn uptake and homeostasis in plants via auxin-mediated Ca2+ signaling. Our findings highlight the need for further investigation into the crosstalk between Ca2+-mediated signaling and Zn transport in legumes since studies show that Ca2+ application can alleviate symptoms of Zn deficiency110.
Members of Cluster 3 include zinc/iron-regulated transporter-like protein (ZIP) family transporters, such as ZIP4, ZIP5, ZIP11, and IRT3, alongside zinc-responsive bZIP transcription factors (bZIP19 and bZIP23) (Table 7). These ZIP transporters are pivotal for high-affinity acquisition of Zn2+ from the rhizosphere, while also contributing to the transport of other divalent metal ions111 like Mn2+, Fe2+, and Cu2+. Previous studies have demonstrated that bZIP19 and bZIP23 bind zinc deficiency-responsive elements in ZIP gene promoters, facilitating increased Zn acquisition under limiting conditions112,113. Furthermore, double mutants lacking both bZIP19 and bZIP23 display hypersensitivity to Zn deficiency, indicating that these factors work synergistically112. This co-clustering of ZIP transporters with their transcriptional regulators suggests a tightly coordinated Zn deficiency response module, where bZIP19/bZIP23 dynamically amplify ZIP-mediated metal uptake to restore Zn homeostasis while inadvertently increasing the influx of non-target metals, which is a metabolic compromise that may explain the observed Mn2+ and Cd2+ hyperaccumulation in Zn-starved plants114. Moreover, IRT3 functions in Fe2+ uptake and can also transport Zn2+. Overexpression of IRT3 in Arabidopsis leads to elevated Zn levels in shoots and enhanced Fe levels in roots, highlighting its dual role115. The presence of IRT3 in this cluster further emphasizes the evolutionary overlap between Fe and Zn acquisition pathways, revealing how plants repurpose Fe transporters under Zn scarcity to exploit ionic similarities between these micronutrients. Ultimately, the expression of transporters like ZIP4, ZIP5, ZIP11, and IRT3 significantly suppressed under prolonged Zn deficiency (Fig. 8), demonstrating their essential roles in maintaining Zn homeostasis in plants116. This intricate regulation emphasizes the importance of ZIP transporters and bZIP transcription factors in metal ion homeostasis.
Cluster 4 contains proteins involved in intracellular Zn2+ distribution and storage, notably HMA2, MTP1, and ZIF1 (Table 7). HMA2 is a P-type ATPase known for exporting Zn2+ and Cd2+ from root cells into the xylem, enabling long-distance metal translocation117,118,119,120. HMA2 has a closely related sequence to HMA4 and exhibits similar tissue expression and localization, suggesting some redundancy in the functions of these two transporters117. While a single mutant of HMA2 shows no apparent phenotype, the hma2hma4 double mutant exhibits a Zn deficiency phenotype and a decreased Zn concentration in the shoot121. HMA2 can restore Zn2+ transport in yeast mutants lacking Zn exporters, confirming its Zn2+ efflux activity117. HMA2 was significantly suppressed in prolonged Zn deficiency (Fig. 8), suggesting that transcriptional downregulation of HMA2 under chronic Zn scarcity may represent a compensatory mechanism to limit Zn efflux from roots, thereby conserving cellular Zn pools under prolonged stress. This suppression aligns with the plant’s prioritization of root Zn retention over shoot translocation during sustained deficiency, a strategy that may mitigate systemic Zn depletion but could simultaneously impair shoot growth and reproductive success due to insufficient aerial Zn allocation122.
Notably, the co-occurrence of MTP1 and ZIF1 in Cluster 4 suggests a synergistic interplay between efflux (HMA2) and vacuolar sequestration/storage (MTP1, ZIF1) pathways to recalibrate Zn partitioning. MTP1 sequesters Zn2+ into vacuoles to prevent cytosolic toxicity and facilitate Zn accumulation in the leaf123. Localization studies revealed that MTP1 is localized in the tonoplast (vascular membrane) in leaf and root cells and sequestering Zn2+ into vacuoles. On the other hand, ZIF1 encodes a major facilitator superfamily transporter that assists in loading Zn2+ into vacuolar-related compartments, further buffering cytoplasmic Zn2+ levels124. Similar to MTP1, ZIF1 localizes to the tonoplast, where it sequesters Zn2+ or a Zn2+–ligand complex into vacuoles. zif1 mutants accumulate excess Zn in shoots and exhibit Zn hypersensitivity, while ZIF1 overexpression confers enhanced tolerance to Zn deficiency. Interestingly, the zif1mtp1 double mutant reveals that ZIF1 and MTP1 act through distinct vacuolar Zn pathways, emphasizing the importance of ZIF1 in maintaining Zn homeostasis. Also, in zif1hma2hma4 triple mutants, shoot Zn levels are much decreased compared to the wild type and single mutants, but the plants remain sensitive to Zn deficiency, indicating that ZIF1 primarily directs Zn compartmentalization instead of merely regulating metal uptake124. Thus, ZIF1 contributes a distinct layer of Zn homeostasis critical for avoiding Zn toxicity while ensuring adequate micronutrient supply122. Collectively, these proteins facilitate the movement and cellular compartmentalization of Zn2+, thereby protecting crucial metabolic processes from metal imbalances. under chronic deficiency, the concurrent suppression of HMA2 and potential upregulation of MTP1/ZIF1 might trap Zn in root vacuoles (Fig. 8), creating a “metabolic sink” that exacerbates shoot Zn limitation. This dynamic between efflux and vacuolar sequestration highlight the tension between Zn conservation and distribution, a trade-off that may explain the stunted growth phenotypes observed in plants subjected to long-term Zn deprivation125.
Proteins in Cluster 5 are primarily responsible for Fe²⁺ uptake, transport, and storage, but they also facilitate the transport of other metals, including Zn2+, Mn2+, and Cd2+ (Table 7). Among these, IRT1 was identified as the main high-affinity Fe2+ transporter, localized to the plasma membrane of root epidermal cells. Plants lacking IRT1 exhibit severe Fe deficiency symptoms, stressing its critical role in Fe acquisition126. Although IRT1 is well-known for transporting Fe and stabilizes Zn2+ via a specific extracellular loop127our in-silico expression analyses showed that its expression is not strongly affected by Zn deficiency or in bzip19bzip23 and hma2hma4 mutants (Fig. 8), suggesting that IRT1’s regulation is primarily driven by Fe homeostasis rather than Zn status. This finding highlights the complexity of metal homeostasis networks in plants. Additionally, ferric reductases (FROs), such as FRO1 and FRO2, facilitate the reduction of insoluble Fe2+ to Fe2+ at the root surface128enabling uptake through IRT1. However, their expression was not altered by Zn deficiency (Fig. 8), indicating their Fe specificity. NRAMP3 is a metal transporter mainly involved in mobilizing Fe2+ from vacuoles during germination and may also have potential Zn transport activity129. However, like IRT1 and FROs, its expression was not affected strongly under Zn deficiency (Fig. 8), suggesting that it is specific to Fe transport.
Another critical protein in in Cluster 5, FRD3 exports citrate into the xylem to enhance Fe solubility and transport, while also aiding Zn2+ translocation from roots to shoots. We showed that FRD3 expression significantly decreased in long Zn deficiency (Fig. 8), suggesting that this downregulation reflects a strategic reallocation of resources to prioritize Zn retention in roots or restrict non-essential metal mobilization under nutrient stress. Impaired FRD3 function leads to excessive Zn accumulation in roots and increased Fe deficiency signals130. This paradoxical phenotype highlights FRD3’s dual role as a rheostat for both Fe and Zn homeostasis, balancing xylem loading of metal-chelating citrate with the prevention of root Zn hyperaccumulation. The Zn deficiency-induced suppression of FRD3 implies an adaptive mechanism to limit citrate-mediated Zn efflux from roots when external Zn is scarce, inadvertently exacerbating Fe immobility due to diminished citrate availability for Fe2+ solubilization. Additionally, members of the NICOTIANAMINE SYNTHASE (NAS) family (NAS1-5) in Cluster 5 produce nicotianamine, a critical metal chelator that forms soluble complexes with divalent cations like Fe2+, Zn2+, and Mn2+ for transport within plants131. NAS proteins interact with other transporters, refining metal partitioning under stress132. These nicotianamine-metal complexes are transported through vascular tissues via Yellow Stripe-Like (YSL) transporters133such as YSL2. Overexpression of NAS3 or NAS2 in rice enhances nicotianamine and Zn accumulation in leaves and seeds134,135. In Triticum monococcum, eight novel NAS genes were upregulated under Zn deficiency, indicating their role in Zn homeostasis136. Based on in silico transcriptomics analysis, all NAS genes were significantly downregulated during prolonged Zn deficiency while YSL2 was slightly suppressed (Fig. 8), suggesting that sustained Zn deprivation triggers a systemic reprogramming of metal allocation, favoring root retention of Zn2+ over its long-distance transport to aerial tissues. This transcriptional suppression of NAS-YSL2-mediated transport pathways under chronic Zn deficiency implies a metabolic trade-off between metal homeostasis and stress adaptation. While short-term Zn scarcity induces NAS upregulation to mobilize stored Zn pools (Fig. 8), prolonged deficiency may prioritize root Zn retention to sustain critical cellular processes, even at the expense of reproductive fitness via reduced seed Zn loading. The coordinated downregulation of NAS and YSL2 could reflect a feedback mechanism to prevent futile cycling of scarce Zn2+ reserves into vascular tissues when environmental availability remains critically low. Paradoxically, this adaptive response may inadvertently exacerbate Zn deficiency in developing seeds, posing challenges for grain nutritional quality in Zn-poor soils137. These findings align with the dual role of nicotianamine as both a metal buffer and a signaling molecule, potentially integrating Zn status with systemic demand.
VIT1, which was identified as a putative transporter associated with Zn accumulation in common bean seeds in this study, sequesters Fe2+ into vacuoles and functions as a proton-coupled antiporter for Fe2+, Zn2+, and Co2+74. vit mutants showed impaired transport74 for Fe2+, Zn2+, and Co2+. In parallel to our expression results of hma2hma4 mutants under long Zn deficiency treatment, expression of VIT1 in root was decreased while shoot expression increased (Fig. 8), suggesting a tissue-specific reprogramming of metal sequestration to balance Zn allocation under chronic deficiency.
Collectively, this study integrates molecular networks governing Zn homeostasis in common bean, revealing a hierarchical coordination of transporters and regulators across distinct functional clusters. The specialized roles of transporters and transcription factors facilitate Zn uptake, distribution, and storage across plant tissues, essential for seed Zn accumulation. Coordinated expression of ZIP transporters (Cluster 3), vacuolar loaders (Cluster 4), and Fe/Zn transporters (Cluster 5) ensure efficient Zn absorption and allocation. Lilay et al.113 demonstrated that bZIP19 and bZIP23 influenced seed Zn levels, with alterations in bZIP19’s Zn-sensor motif leading to a 50% increase in seed Zn concentration. Additionally, Lee et al.134 found redundancy among ZIP transporters (IRT3, ZIP4, ZIP6, and ZIP9) in maintaining Zn homeostasis and seed development. These findings represent known regulatory mechanisms of Zn homeostasis and accumulation in plants. Furthermore, the interplay between Cluster 1’s cell wall dynamics (e.g., TCH4/XTH22-mediated xyloglucan remodeling) and Cluster 2’s GLR-Ca2+ signaling may represent a previously unrecognized regulatory layer in Zn homeostasis. For instance, cell wall Zn binding could buffer cytosolic Zn2+ fluctuations, while GLR-mediated Ca2+ influx might phosphorylate Zn transporters like ZIPs or HMAs to optimize their activity under deficiency. This crosstalk between structural adaptations (Cluster 1) and signaling networks (Cluster 2) could explain how plants reconcile localized Zn retention with systemic demand. Our identification of the Zn-associated SNP Pv08 and functional validation of transporters like VIT1 provide actionable targets for enhancing seed Zn.
Conclusion
This study provides critical insights into the genetic regulation of Zn accumulation in Turkish common bean germplasm, utilizing genome-wide association studies to identify key genetic loci and candidate genes. The significant variation in Zn concentration across genotypes highlights the potential for genetic improvement through selective breeding. Identified marker-trait associations on chromosomes Pv06 and Pv08, along with candidate genes VIT1 and WAKL4, suggest important roles in Zn transport and cell-wall remodeling under Zn-deficient conditions. In silico transcriptomics and protein interaction analyses further support the involvement of these genes in Zn homeostasis. The findings contribute to the growing body of research on biofortification, demonstrating how genomic tools can enhance nutrient concentration in staple crops. Future breeding programs can leverage these genetic insights to develop high-Zn common bean varieties, addressing global micronutrient deficiencies and improving food security.
Data availability
All data necessary for conducting this study is included in the manuscript and supplementary materials.
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Funding
The authors express their gratitude to the Scientific and Technological Research Council of Turkey (TÜBİTAK) for this research through grant number TOVAG-2015O630 awarded to FSB. This study has been supported by the Recep Tayyip Erdoğan University Development Foundation (Grant Number: 02025002007283). This research was also supported by Brain Pool program funded by the Ministry of Science and ICT through the National Research Foundation of Korea (RS-2024-00403759) to FSB.
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M.T.A: Writing—original draft, formal analysis, Funding acquisition. MAN: Writing—review & editing, Software, Formal analysis, Visualization, Validation. S.A: Writing—original draft, Formal analysis, Visualization. E.A: Writing—original draft, review & editing, Formal analysis, Visualization. S.M: Writing—review & editing, formal analysis. E.M.B.M.K: Writing—review & editing, formal analysis. YSC: Writing—review & editing, formal analysis. FSB: Writing—review & editing, Conceptualization, Project administration, Supervision, Funding acquisition, Validation.
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Altaf, M.T., Nadeem, M.A., Ayten, S. et al. Genome-wide association study of common bean (Phaseolus vulgaris L.) unveils novel loci governing root-to-seed zinc allocation. Sci Rep 15, 21503 (2025). https://doi.org/10.1038/s41598-025-07965-9
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DOI: https://doi.org/10.1038/s41598-025-07965-9
