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Article

Disruption of FW2.2-like Genes Enhances Metallic Micronutrient Accumulation in Brown Rice

by
Qingsong Gao
1,
Rumeng Sun
2,
Jiayi Ding
1,
Xingdang Xu
1,
Xun Ma
1,
Xi Liu
1 and
Hao Zhang
2,*
1
Jiangsu Key Laboratory for Eco-Agricultural Biotechnology Around Hongze Lake, Jiangsu Collaborative Innovation Center of Regional Modern Agriculture & Environmental Protection, Huaiyin Normal University, Huaian 223300, China
2
Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Key Laboratory of Crop Cultivation and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(7), 1747; https://doi.org/10.3390/agronomy15071747
Submission received: 6 May 2025 / Revised: 16 July 2025 / Accepted: 18 July 2025 / Published: 20 July 2025
(This article belongs to the Special Issue Innovative Research on Rice Breeding and Genetics)
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Abstract

Micronutrient deficiencies adversely affect human health and pose a significant global threat. Enhancing the accumulation of micronutrients in the edible parts of crops through genetic breeding is a promising strategy to mitigate micronutrient deficiencies in humans. FW2.2-like (FWL) genes play crucial roles in regulating heavy metal homeostasis in plants. We previously obtained two allelic mutants for each of the rice OsFWL1 (osfwl1a and osfwl1b) and OsFWL2 (osfwl2a and osfwl2b) genes. In this study, we showed that disruption of either OsFWL1 or OsFWL2 significantly enhanced the accumulation of metallic micronutrients in brown rice. Compared with that in the wild type, the iron (Fe) concentration in brown rice was higher in the osfwl1a (+166.7%), osfwl1b (+24.3%), and osfwl2a (+99.2%) mutants; the manganese (Mn) concentration was elevated in all four mutants (+25.1% to 35.6%); the copper (Cu) concentration increased in osfwl2a (+31.0%) and osfwl2b (+29.0%); and the zinc (Zn) concentration increased in osfwl2a (+10.2%). Additionally, disruption of OsFWL1 or OsFWL2 affected the homeostasis of metallic micronutrients in seedlings. Transcriptome analysis suggested that OsFWL1 and OsFWL2 might regulate cell wall polysaccharide metabolism and the expression of heavy metal transporter genes. Protein interaction analysis revealed that OsFWL1 interacted with OsFWL2 on the cell membrane. These findings suggest that OsFWL1 and OsFWL2 can serve as genetic biofortification tools to increase the concentrations of metallic micronutrients in rice grains.

1. Introduction

Metallic micronutrients such as iron (Fe), manganese (Mn), copper (Cu), and zinc (Zn) are essential for both plant growth and human health [1]. The recommended daily intake of Fe, Mn, Cu, and Zn for humans is 15 mg–20 mg, 2.5 mg–7 mg, 2 mg, and 11.5 mg–15 mg, respectively [2]. However, a significant portion of the global population experiences deficiencies in these micronutrients. For instance, it is estimated that approximately 65% of the global population is deficient in Fe, and about 46% is deficient in Zn [3]. Cereals are staple foods of humans. Developing new cereal varieties that accumulate high concentrations of metallic micronutrients in grains is an effective strategy to alleviate micronutrient deficiencies in humans [4]. However, traditional crop breeding techniques are time-consuming and difficult. In contrast, biotechnology offers a relatively rapid approach for breeding micronutrient-rich cultivars.
FW2.2-like (FWL) genes encode cysteine-rich proteins and play important roles in plant growth, development, and heavy metal homeostasis [5]. These genes are also referred to as Cell number regulator (CNR) or Plant cadmium resistance (PCR) genes in the literature. AtPCR1 regulates cadmium (Cd) resistance by reducing Cd uptake in Arabidopsis thaliana [6]. AtPCR2 encodes a Zn transporter and is involved in Zn redistribution and detoxification [7]. In Oryza sativa, OsPCR1 regulates Zn accumulation and grain weight [8]. OsPCR1 and OsPCR3 are critical for Cd tolerance and accumulation [9]. OsFWL4 is involved in the transport of Cd from roots to shoots [10]. OsFWL7 influences Cd and metallic micronutrient accumulation [11]. Additionally, the overexpression of either the Triticum aestivum genes TaCNR2 and TaCNR5, or the Triticum urartu gene TuCNR10, in O. sativa and Arabidopsis enhances tolerance to heavy metals, and the overexpression of these genes in O. sativa increases the concentrations of metallic micronutrients in the grains [12,13,14]. However, the molecular mechanisms by which FWL genes regulate heavy metal homeostasis remain unclear. Recently, Solanum lycopersicum FW2.2, the founding member of the FWL family, was demonstrated to accumulate at plasmodesmata (PD) [15]. FW2.2 negatively regulates callose deposition at PD by inhibiting callose synthase activity, thus increasing PD permeability.
O. sativa is one of the most important cereal crop species worldwide [16]. To explore the potential roles of O. sativa FWL genes in metallic micronutrient accumulation, we previously designed two target sites for each of the eight O. sativa FWL genes (OsFWL1–OsFWL8) and generated gene-edited mutants using the CRISPR/Cas9 system [17]. In this study, we showed that mutation of the OsFWL1 or OsFWL2 gene significantly enhanced metallic micronutrient accumulation in brown rice. Furthermore, the homeostasis of metallic micronutrients in seedlings of the OsFWL1 and OsFWL2 gene mutants was also affected. Transcriptome analysis revealed that OsFWL1 and OsFWL2 might regulate cell wall polysaccharide metabolism and the expression of heavy metal transporter genes.

2. Materials and Methods

2.1. Plant Materials and Treatments

The osfwl1a, osfwl1b, osfwl2a, and osfwl2b mutants were obtained previously [17]. The O. sativa japonica variety Zhonghua 11 was used as the wild-type (WT) control. Both WT and mutant plants were cultivated in paddy fields at Huaiyin Normal University in the summer. Additionally, O. sativa plants were grown in Yoshida solution in a growth chamber (RXM-508F-3; Jiangnan Instrument, Ningbo, China) under a 14/10 h light/dark cycle at 30 °C/25 °C (day/night). The relative humidity was maintained at 70%, and the light intensity was set at 600 μm m−2 × s−1.

2.2. Subcellular Localization Analysis

The coding sequence (CDS) of OsFWL1 or OsFWL2 was inserted into the pAN580-GFP vector and transformed into O. sativa protoplasts [18]. The AtNAA60 protein was fused with mkate and used as a plasma membrane localization marker [19]. The protoplasts were imaged 1 d after transformation with a Nikon C2-ER confocal laser scanning microscope (Nikon, Tokyo, Japan). The PCR primers used are shown in Table S8.

2.3. BiFC Assay

The CDSs of OsFWL1 and OsFWL2 were inserted into the 62SK-VN and 62SK-VC vectors, respectively. The constructs were subsequently transformed into Agrobacterium tumefaciens strain GV3101, and the resulting strains were inoculated into Nicotiana benthamiana leaves. The leaves were imaged 2 d after inoculation with a confocal laser scanning microscope (Nikon C2-ER). The PCR primers used are shown in Table S8.

2.4. RNA Sequencing (RNA-Seq) Analysis

Total RNA was isolated with TRIzol reagent (Invitrogen, Carlsbad, CA, USA). After quality evaluation, the mRNA was enriched with oligo(dT) beads. The mRNA was subsequently fragmented and reverse-transcribed into complementary DNA (cDNA). Sequencing adapters were ligated to the purified double-stranded cDNA fragments, which were then subjected to size selection before PCR amplification. The cDNA library was sequenced on an Illumina NovaSeq 6000 at Gene Denovo Biotechnology Co., Ltd. (Guangzhou, China). Three biological replicates were utilized for each genotype.
The clean reads were mapped to the O. sativa reference genome (Ensembl_release53_IRGSP-1.0_Nipponbare) using HISAT2.2.4 [20]. The expression abundance and variations in each transcription region were quantified by calculating the fragment per kilobase of transcript per million mapped reads (FPKM) value. The differential expression analysis of the transcripts was performed using DESeq2 [21]. The parameters were set as follows: fold change log2 ratio >1 or <−1 and false discovery rate (FDR) <0.05. The RNA-seq data were deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive database (accession number: PRJNA1285298).

2.5. Reverse-Transcription Quantitative PCR (RT-qPCR)

Total RNA (0.8 μg) was reverse-transcribed with the PrimeScript RT Reagent Kit with gDNA Eraser (Takara, Dalian, China). PCR amplification was conducted using TB Green Premix Ex Taq II (Takara) in a CFX Connect Real-Time PCR instrument (Bio-Rad, Hercules, CA, USA) with 40 cycles. Three biological replicates were used for each genotype. Melting curve analysis of the amplicons was performed to verify the specificity of the PCR. The O. sativa Ubiquitin gene was used as an internal reference [22]. The primers used are shown in Table S8.

2.6. Measurement of Heavy Metal Concentrations

The dried tissues were ground into a fine powder with a Cole-Parmer analytical mill (Vernon Hills, IL, USA), and 0.4 g samples were digested with HNO3 and H2O2 in a MARS 5 microwave dissolver (CEM, Matthews, NC, USA). Heavy metal levels were determined using inductively coupled plasma-optical emission spectrometry (ICP–OES, iCAP 6300; Thermo Fisher Scientific, Grand Island, NY, USA) following the methods described by Zhang et al. [16]. For the analysis of brown rice and husk tissues, two biological replicates and three technical replicates were used. For seedling root and shoot tissues, three technical replicates were used.

2.7. Statistical Analysis

All data are presented as means ± standard deviations of the replicates. One-way ANOVA was performed using IBM SPSS Statistics version 23. Column charts were generated with SigmaPlot 10.0.

3. Results

3.1. Disruption of OsFWL1 or OsFWL2 Increases the Concentrations of Metallic Micronutrients in Brown Rice

Two allelic mutant lines that were homozygous and transgene-free were selected for each of the OsFWL1 and OsFWL2 genes. These mutant lines were named osfwl1a and osfwl1b for OsFWL1, and osfwl2a and osfwl2b for OsFWL2. The osfwl1a and osfwl2a mutants each contained a 1 bp (base pair) insertion at their respective target sites, whereas the osfwl1b mutant had a 1 bp deletion, and osfwl2b had a 2 bp deletion (Figure S1). All these mutations induced frameshifts in the respective genes. The WT and mutant plants were cultivated in paddy fields with standard fertilizer application (240 kg × ha−1 urea, 300 kg × ha−1 calcium superphosphate, and 195 kg × ha−1 potassium chloride). The grain weight of the osfwl1a, osfwl1b, osfwl2a, and osfwl2b mutants did not significantly differ from that of the WT (Figure S2). The heavy metal levels in the grains were measured using ICP–OES. The Fe concentration in brown rice was markedly higher in osfwl1a (+166.7%), osfwl1b (+24.3%), and osfwl2a (+99.2%) than in the WT (Figure 1A). The Mn concentration in brown rice increased in all four mutants (+25.1% to 35.6%; Figure 1B). The Cu concentration in brown rice was elevated in osfwl2a (+31.0%) and osfwl2b (+29.0%) (Figure 1C), whereas the Zn concentration in brown rice increased only in osfwl2a (+10.2%; Figure 1D). In husks, the Fe concentration significantly increased in osfwl2b but decreased in the other three mutants (Figure 1A). The Mn concentration increased in osfwl1a, osfwl2a, and osfwl2b (Figure 1B). The Cu concentration increased in osfwl1a, osfwl2a, and osfwl2b but decreased in osfwl1b (Figure 1C). The Zn concentration increased in osfwl2a and osfwl2b (Figure 1D). Collectively, these results suggest that mutation of OsFWL1 or OsFWL2 enhances the accumulation of metallic micronutrients in brown rice.
Cd is a highly toxic heavy metal. To test whether the mutation of OsFWL1 or OsFWL2 affects Cd accumulation in O. sativa grains, the Cd level was measured. Compared with that in the WT, the Cd concentration in brown rice was significantly lower in the osfwl1a, osfwl1b, and osfwl2a mutants, but slightly higher in osfwl2b (Figure 1E). The Cd concentration in husks was markedly reduced in all four mutants.

3.2. OsFWL1 and OsFWL2 Influence the Homeostasis of Metallic Micronutrients in O. sativa Seedlings

To understand the physiological mechanisms underlying changes in metallic micronutrient concentrations in grains of the OsFWL1 and OsFWL2 gene mutants, the WT and mutant plants were cultivated hydroponically, and the concentrations of metallic micronutrients in the roots and shoots were determined. After two weeks of growth, the shoot length of osfwl1a and osfwl1b was comparable to that of the WT, whereas the shoot of osfwl2a and osfwl2b was shorter (Figure 2A,B). The root length of mutants was similar to that of the WT (Figure 2A,C). However, the dry weights of both shoots and roots were reduced in all mutants compared with the WT (Figure 2D,E).
The determination of metallic micronutrient levels revealed that the Fe concentration in the shoots was significantly higher in osfwl1b and osfwl2b but slightly lower in osfwl1a compared with the WT (Figure 3A). The Fe concentration in the roots was reduced in all four mutants. The Mn concentration decreased in the shoots of osfwl1a, osfwl2a, and osfwl2b, as well as in the roots of osfwl1a and osfwl2a (Figure 3B). The Cu concentration was lower in the shoots of all mutants and in the roots of osfwl1b, osfwl2a, and osfwl2b (Figure 3C). In contrast, the Zn concentration increased in both the shoots and roots of all four mutants (Figure 3D).
To test whether the mutation of OsFWL1 or OsFWL2 affects the translocation of metallic micronutrients in seedlings, we analyzed the shoot-to-root ratios of the concentrations of metallic micronutrients. The shoot-to-root ratio of Fe was significantly higher in osfwl1b, osfwl2a, and osfwl2b than in the WT (Figure 4A). For Mn, the ratio was higher in osfwl1a and osfwl2a but lower in osfwl1b and osfwl2b (Figure 4B). The shoot-to-root ratio of Cu was reduced in all four mutants (Figure 4C). The shoot-to-root ratio of Zn was slightly higher in osfwl1a but lower in the remaining mutants (Figure 4D). Collectively, these findings suggest that mutation of OsFWL1 or OsFWL2 affects the homeostasis of metallic micronutrients in O. sativa seedlings.

3.3. Transcriptome Analysis of the OsFWL1 and OsFWL2 Gene Mutants

To elucidate the molecular mechanisms by which OsFWL1 and OsFWL2 regulate metallic micronutrient homeostasis, we performed transcriptome analysis on the corresponding gene mutants. The osfwl1b and osfwl2a mutants were used for OsFWL1 and OsFWL2, respectively. The WT and mutant plants were grown in the hydroponic solution for one week. The root tissues were then sampled with three biological replicates for RNA-seq.
RNA-seq yielded over 568 million reads in total. For each sample, at least 99.2% of the reads were clean reads, and ≥68.6% of the clean reads could be mapped to a unique location in the O. sativa reference genome. The Pearson correlation coefficient between biological replicates was greater than 0.83 (Figure S3).
The differentially expressed genes (DEGs) between the mutants and WT plants were screened according to the following criteria: fold change log2 ratio >1 or <−1 and FDR <0.05. The numbers of DEGs from the osfwl1b versus WT comparison (named DEGs_osfwl1b hereafter) and the osfwl2a versus WT comparison (named DEGs_osfwl2a hereafter) were 818 and 1146, respectively (Tables S1 and S2, Figure S4). To understand the functions of the DEGs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. GO analysis revealed numerous significant (p < 0.05) terms in the biological process (BP), molecular function (MF), and cellular component (CC) categories (Tables S3 and S4). Among the most enriched GO terms for DEGs_osfwl1b, cell wall organization or biogenesis, external encapsulating structure organization, and polysaccharide metabolic process, among others, were identified in the BP category (Figure 5A); protein heterodimerization activity, manganese ion binding, transition metal ion binding, and hydrolase activity–hydrolyzing O-glycosyl compounds, among others, were identified in the MF category (Figure 5B); and the extracellular region, cell wall, and external encapsulating structure, among others, were identified in the CC category (Figure 5C). Among the most enriched GO terms for DEGs_osfwl2a, cell wall organization or biogenesis, external encapsulating structure organization, polysaccharide metabolic process, and cell wall macromolecule metabolic process, among others, were identified in the BP category (Figure 6A); hydrolase activity–hydrolyzing O-glycosyl compounds, protein heterodimerization activity, zinc ion transmembrane transporter activity, and transition metal ion transmembrane transporter activity, among others, were identified in the MF category (Figure 6B); and the cell periphery, extracellular region, and cell wall, among others, were identified in the CC category (Figure 6C).
KEGG analyses revealed that the biosynthesis of various plant secondary metabolites, metabolic pathways, and the mitogen-activated protein kinase (MAPK) signaling pathway–plant were among the top 20 pathway terms for DEGs_osfwl1b (Figure 5D, Table S5), and the biosynthesis of various plant secondary metabolites and metabolic pathways were among the top 20 pathway terms for DEGs_osfwl2a (Figure 6D, Table S6).
A Venn diagram analysis revealed that 398 DEGs were shared between DEGs_osfwl1b and DEGs_osfwl2a (Figure S5). A KEGG analysis revealed that these shared DEGs were involved in pathways such as the biosynthesis of various plant secondary metabolites and starch and sucrose metabolism (Table S7).

3.4. Validation of RNA-Seq Data Using RT-qPCR

To validate the RNA-seq data, we performed RT-qPCR assays on several genes involved in cell wall polysaccharide metabolism and heavy metal transport. The trends in the changes of gene expression determined by RT-qPCR were generally congruent with the RNA-seq data (Figure 7 and Figure S6), suggesting that the RNA-seq results are reliable.
OsGLN1 encodes a β-1,3-glucanase that hydrolyzes the cell wall β-glucans from fungi in vitro [23]. The expression level of OsGLN1 significantly increased in the osfwl1b mutant compared with the WT (Figure 7A). OsBGL3 encodes a β-glucanase that promotes callose deposition at PD [24]. The expression level of OsBGL3 decreased in the osfwl2a mutant. OsEGL1 encodes a β-1,3-1,4-glucanase that specifically hydrolyzes β-1,3-1,4-glucans in the cell wall [25]. The expression level of OsEGL1 increased in both the osfwl1b and osfwl2a mutants. Os06g0625700 encodes a putative cellulose synthase-like protein that is thought to catalyze the synthesis of hemicellulosic polysaccharides [26]. The expression level of Os06g0625700 decreased in both mutants (Figure 7A). Collectively, these results suggest that mutation of OsFWL1 or OsFWL2 affects the expression of genes involved in cell wall polysaccharide metabolism.
OsIRT1 is an Fe transporter involved in Fe uptake [27]. The expression level of OsIRT1 significantly decreased in the osfwl1b and osfwl2a mutants (Figure 7B). OsNRAMP3 is involved in the distribution of Mn [28]. OsNRAMP5 is a major transporter for Mn uptake, translocation, and distribution [29,30]. OsNRAMP6 functions as a Mn and Fe transporter [31]. The expression levels of OsNRAMP3 and OsNRAMP6 did not significantly change in the osfwl1b and osfwl2a mutants, but that of OsNRAMP5 increased. OsCOPT1 and OsHMA5 are associated with Cu distribution [32,33]. The expression levels of both genes were elevated in the osfwl1b and osfwl2a mutants. OsZIP1 is an exporter of Zn and Cu [34]. OsZIP4 is important for Zn distribution [35]. OsHMA2 influences the root-to-shoot translocation of Zn [36]. The expression level of OsZIP4 did not significantly change in the osfwl1b and osfwl2a mutants, but that of OsZIP1 increased. In addition, the expression level of OsHMA2 slightly increased in osfwl2a (Figure 7B). These results suggest that mutation of OsFWL1 or OsFWL2 influences the expression of heavy metal transporter genes.

3.5. OsFWL1 Interacts with OsFWL2 on the Cell Membrane

Subcellular localization assays showed that both OsFWL1 and OsFWL2 proteins were localized to the cell membrane (Figure S7), which is consistent with a previous report [37]. A bimolecular fluorescence complementation (BiFC) assay revealed that OsFWL1 and OsFWL2 physically interacted on the cell membrane in N. benthamiana leaves (Figure 8). These results suggest that OsFWL1 and OsFWL2 cooperatively regulate metallic micronutrient homeostasis at the cell membrane.

4. Discussion

Deficiencies in micronutrients in humans, known as “hidden hunger”, represent a global nutritional issue. Rice is the staple food for more than half of the global population. Hence, developing micronutrient-efficient rice cultivars could help alleviate micronutrient malnutrition. In this study, the concentrations of Fe and Mn in brown rice were significantly higher in the osfwl1a and osfwl1b mutants than in the WT (Figure 1). Although the concentrations of Cu and Zn slightly decreased in osfwl1a, its Fe concentration increased by 166.7% (reaching 138.7 mg × kg−1). The concentrations of Fe, Mn, Cu, and Zn in brown rice increased in osfwl2a, and the concentrations of Mn and Cu increased in osfwl2b (Figure 1). Moreover, the Cd concentration in brown rice was reduced in the osfwl1a, osfwl1b, and osfwl2a mutants compared with the WT (Figure 1). All four mutants (osfwl1a, osfwl1b, osfwl2a, and osfwl2b) were generated using the CRISPR/Cas9 technology and were transgene-free [17]. These findings suggest that the OsFWL1 and OsFWL2 genes can be used as genetic tools for biofortification in O. sativa.
The levels of metal elements in cereal grains are dependent on several physiological processes: root uptake, subsequent transport to shoots, and further redistribution to developing grains. Interestingly, different patterns of accumulation were observed for different metallic micronutrients in seedlings of the OsFWL1 and OsFWL2 gene mutants. For example, the Zn concentration was significantly higher in all mutants than in the WT, whereas the Cu concentration was lower (Figure 3). The root-to-shoot translocation of Fe was increased in osfwl1b, osfwl2a, and osfwl2b, whereas that of Cu was reduced in all four mutants (Figure 4). In addition, there seemed to be no clear relationship between the uptake and translocation of metallic micronutrients in seedlings of the OsFWL1 and OsFWL2 gene mutants and their accumulation in brown rice. A possible reason for this may be that plant FWL proteins perform distinct biological functions in different tissues. For instance, AtPCR2 is involved in Zn transport from roots to shoots in vascular tissues and in the extrusion of excess Zn in epidermal cells [7]. The function of AtPCR2 is also dependent on tissue maturity. Similarly, mutation of OsFWL3 was reported to increase Zn and Mn concentrations but reduce Fe and Mg concentrations in brown rice [38]. The uptake of Mn and the translocation of Zn from roots to shoots were reduced in the seedlings of the OsFWL3 gene mutants.
The plant cell wall is primarily composed of structural polysaccharides such as cellulose, hemicellulose, and pectin, lignin, and several proteins, and it plays various fundamental roles, including nutrient transport [26]. However, the biosynthesis and function of the plant cell wall remain poorly understood. Callose is a polysaccharide that accumulates in cell wall microdomains. Recently, S. lycopersicum FW2.2 was found to regulate callose accumulation at PD and thus PD permeability [15]. Symplastic transport through PD plays important roles in the uptake and translocation of metal elements [39]. Hence, the modification of PD permeability influences metal homeostasis. OsFWL1 and OsFWL2 are the two closest homologs of S. lycopersicum FW2.2 in O. sativa [37]. Transcriptome analysis revealed that terms such as “cell wall organization or biogenesis”, “polysaccharide metabolic process”, and “hydrolase activity–hydrolyzing O-glycosyl compounds” were among the most enriched GO terms for both DEGs_osfwl1b and DEGs_osfwl2a (Figure 5 and Figure 6). RT-qPCR confirmed that the expression of genes involved in cell wall polysaccharide metabolism was altered in the OsFWL1 and OsFWL2 gene mutants (Figure 7A). These results indicate that OsFWL1 and OsFWL2 may participate in polysaccharide metabolism in the cell wall and indirectly affect heavy metal homeostasis.
Membrane-embedded transporters play essential roles in the transmembrane transport of heavy metals [39]. Both OsFWL1 and OsFWL2 proteins are localized to the cell membrane and may form a complex (Figure S7 and Figure 8). Transcriptome analysis revealed that terms associated with transition metal ion binding or transport were among the most enriched MF terms for both DEGs_osfwl1b and DEGs_osfwl2a (Figure 5B and Figure 6B). RT-qPCR confirmed that the expression of heavy metal transporter genes was altered in the osfwl1b and osfwl2a mutants (Figure 7B). Hence, OsFWL1 and OsFWL2 may also modulate metallic micronutrient homeostasis by influencing the expression of heavy metal transporter genes.

5. Conclusions

Breeding cereal cultivars with an enhanced ability to accumulate micronutrients in grains represents an effective strategy for mitigating micronutrient deficiencies. FWL genes play crucial roles in heavy metal accumulation in plants and hold significant potential as tools for genetic biofortification. However, the mechanisms by which FWL genes regulate heavy metal accumulation remain poorly understood. In this study, we showed that loss of function of OsFWL1 or OsFWL2 significantly enhanced the accumulation of metallic micronutrients in brown rice. OsFWL1 and OsFWL2 may form a complex on the cell membrane and regulate metallic micronutrient homeostasis by affecting cell wall polysaccharide metabolism and the expression of heavy metal transporter genes. Our results lay a solid foundation for the genetic biofortification of rice by manipulating the OsFWL1 and OsFWL2 genes.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agronomy15071747/s1: Figure S1: Molecular identification of the OsFWL1 and OsFWL2 gene mutants; Figure S2: Grain weight of the osfwl1a, osfwl1b, osfwl2a, and osfwl2b mutants; Figure S3: Heatmap of correlation coefficients between gene expression values of samples in the RNA-seq assay; Figure S4: Numbers of DEGs identified from the osfwl1b versus WT and osfwl2a versus WT comparisons; Figure S5: Venn diagram analysis showing the numbers of shared and unique DEGs; Figure S6: Expression analysis of genes involved in cell wall polysaccharide metabolism (A) and heavy metal transport (B) using RNA-seq data; Figure S7: Subcellular localization assays of OsFWL1 and OsFWL2; Table S1: DEGs identified from the osfwl1b versus WT comparison; Table S2: DEGs identified from the osfwl2a versus WT comparison; Table S3: Gene Ontology enrichment analysis of DEGs identified from the osfwl1b versus WT comparison; Table S4: Gene Ontology enrichment analysis of DEGs identified from the osfwl2a versus WT comparison; Table S5: KEGG pathway enrichment analysis of DEGs identified from the osfwl1b versus WT comparison; Table S6: KEGG pathway enrichment analysis of DEGs identified from the osfwl2a versus WT comparison; Table S7: KEGG pathway enrichment analysis of shared DEGs between the osfwl1b versus WT and osfwl2a versus WT comparisons; Table S8: Primers used in this study.

Author Contributions

Conceptualization, H.Z.; methodology, Q.G. and H.Z.; investigation, Q.G., R.S., J.D., X.X., X.M. and X.L.; data curation, Q.G., R.S., J.D., X.X., X.M. and X.L.; writing—original draft preparation, Q.G.; writing—review and editing, H.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (32071944), the Natural Science Research Program of Huai’an Municipality (HAB202155), and the Postgraduate Research and Practice Innovation Program of Jiangsu Province (SJCX24_2121).

Data Availability Statement

Data is contained within the article and Supplementary Materials.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Determination of the Fe (A), Mn (B), Cu (C), Zn (D), and Cd (E) concentrations in brown rice and husks of the osfwl1a, osfwl1b, osfwl2a, and osfwl2b mutants. Vertical bars represent the means ± SDs (n = 6). Different lowercase letters above the columns indicate significant differences (p < 0.05).
Figure 1. Determination of the Fe (A), Mn (B), Cu (C), Zn (D), and Cd (E) concentrations in brown rice and husks of the osfwl1a, osfwl1b, osfwl2a, and osfwl2b mutants. Vertical bars represent the means ± SDs (n = 6). Different lowercase letters above the columns indicate significant differences (p < 0.05).
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Figure 2. Phenotypes of the osfwl1a, osfwl1b, osfwl2a, and osfwl2b mutants grown in the hydroponic solution. (A) Plants of the osfwl1a, osfwl1b, osfwl2a, and osfwl2b mutants. Scale bars, 5 cm. (B–E) Shoot length (B), root length (C), shoot dry weight (D), and root dry weight (E) of the osfwl1a, osfwl1b, osfwl2a, and osfwl2b mutants. Vertical bars represent the means ± SDs (n = 16). Different lowercase letters above the columns indicate significant differences (p < 0.05).
Figure 2. Phenotypes of the osfwl1a, osfwl1b, osfwl2a, and osfwl2b mutants grown in the hydroponic solution. (A) Plants of the osfwl1a, osfwl1b, osfwl2a, and osfwl2b mutants. Scale bars, 5 cm. (B–E) Shoot length (B), root length (C), shoot dry weight (D), and root dry weight (E) of the osfwl1a, osfwl1b, osfwl2a, and osfwl2b mutants. Vertical bars represent the means ± SDs (n = 16). Different lowercase letters above the columns indicate significant differences (p < 0.05).
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Figure 3. Determination of the Fe (A), Mn (B), Cu (C), and Zn (D) concentrations in seedlings of the osfwl1a, osfwl1b, osfwl2a, and osfwl2b mutants. Vertical bars represent the means ± SDs (n = 3). Different lowercase letters above the columns indicate significant differences (p < 0.05).
Figure 3. Determination of the Fe (A), Mn (B), Cu (C), and Zn (D) concentrations in seedlings of the osfwl1a, osfwl1b, osfwl2a, and osfwl2b mutants. Vertical bars represent the means ± SDs (n = 3). Different lowercase letters above the columns indicate significant differences (p < 0.05).
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Figure 4. Shoot-to-root ratios of the Fe (A), Mn (B), Cu (C), and Zn (D) concentrations in seedlings of the osfwl1a, osfwl1b, osfwl2a, and osfwl2b mutants. Vertical bars represent the means ± SDs (n = 3). Different lowercase letters above the columns indicate significant differences (p < 0.05).
Figure 4. Shoot-to-root ratios of the Fe (A), Mn (B), Cu (C), and Zn (D) concentrations in seedlings of the osfwl1a, osfwl1b, osfwl2a, and osfwl2b mutants. Vertical bars represent the means ± SDs (n = 3). Different lowercase letters above the columns indicate significant differences (p < 0.05).
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Figure 5. Annotation of DEGs identified in the osfwl1b versus WT comparison. (A–C) Top 20 enriched GO terms in the biological process (A), molecular function (B), and cellular component (C) categories. (D) Top 20 enriched KEGG pathways.
Figure 5. Annotation of DEGs identified in the osfwl1b versus WT comparison. (A–C) Top 20 enriched GO terms in the biological process (A), molecular function (B), and cellular component (C) categories. (D) Top 20 enriched KEGG pathways.
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Figure 6. Annotation of DEGs identified in the osfwl2a versus WT comparison. (A–C) Top 20 enriched GO terms in the biological process (A), molecular function (B), and cellular component (C) categories. (D) Top 20 enriched KEGG pathways.
Figure 6. Annotation of DEGs identified in the osfwl2a versus WT comparison. (A–C) Top 20 enriched GO terms in the biological process (A), molecular function (B), and cellular component (C) categories. (D) Top 20 enriched KEGG pathways.
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Figure 7. Validation of the expression of genes involved in cell wall polysaccharide metabolism (A) and heavy metal transport (B) via RT-qPCR. Vertical bars represent the means ± SDs (n = 3). Different lowercase letters above the columns indicate significant differences (p < 0.05).
Figure 7. Validation of the expression of genes involved in cell wall polysaccharide metabolism (A) and heavy metal transport (B) via RT-qPCR. Vertical bars represent the means ± SDs (n = 3). Different lowercase letters above the columns indicate significant differences (p < 0.05).
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Figure 8. Bimolecular fluorescence complementation analysis showing the interaction between OsFWL1 and OsFWL2 in the leaf cells of N. benthamiana. Scale bars, 20 μm.
Figure 8. Bimolecular fluorescence complementation analysis showing the interaction between OsFWL1 and OsFWL2 in the leaf cells of N. benthamiana. Scale bars, 20 μm.
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MDPI and ACS Style

Gao, Q.; Sun, R.; Ding, J.; Xu, X.; Ma, X.; Liu, X.; Zhang, H. Disruption of FW2.2-like Genes Enhances Metallic Micronutrient Accumulation in Brown Rice. Agronomy 2025, 15, 1747. https://doi.org/10.3390/agronomy15071747

AMA Style

Gao Q, Sun R, Ding J, Xu X, Ma X, Liu X, Zhang H. Disruption of FW2.2-like Genes Enhances Metallic Micronutrient Accumulation in Brown Rice. Agronomy. 2025; 15(7):1747. https://doi.org/10.3390/agronomy15071747

Chicago/Turabian Style

Gao, Qingsong, Rumeng Sun, Jiayi Ding, Xingdang Xu, Xun Ma, Xi Liu, and Hao Zhang. 2025. "Disruption of FW2.2-like Genes Enhances Metallic Micronutrient Accumulation in Brown Rice" Agronomy 15, no. 7: 1747. https://doi.org/10.3390/agronomy15071747

APA Style

Gao, Q., Sun, R., Ding, J., Xu, X., Ma, X., Liu, X., & Zhang, H. (2025). Disruption of FW2.2-like Genes Enhances Metallic Micronutrient Accumulation in Brown Rice. Agronomy, 15(7), 1747. https://doi.org/10.3390/agronomy15071747

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