Next Article in Journal
Application of Three Deep Machine-Learning Algorithms in a Construction Assessment Model of Farmland Quality at the County Scale: Case Study of Xiangzhou, Hubei Province, China
Next Article in Special Issue
Genetic Mapping of the Gamete Eliminator Locus, S2, Causing Hybrid Sterility and Transmission Ratio Distortion Found between Oryza sativa and Oryza glaberrima Cross Combination
Previous Article in Journal
Shoot Girdling Improves Rooting Performance of Kalamata Olive Cuttings by Upregulating Carbohydrates, Polyamines and Phenolic Compounds
Previous Article in Special Issue
Drought Tolerant near Isogenic Lines (NILs) of Pusa 44 Developed through Marker Assisted Introgression of qDTY2.1 and qDTY3.1 Enhances Yield under Reproductive Stage Drought Stress
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agriculture
Volume 11
Issue 1
10.3390/agriculture11010070
agriculture-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Genome-Wide Association Study Using a Multiparent Advanced Generation Intercross (MAGIC) Population Identified QTLs and Candidate Genes to Predict Shoot and Grain Zinc Contents in Rice

by
Shilei Liu
1,2,†,
Wenli Zou
1,2,†,
Xiang Lu
2,
Jianmin Bian
1,
Haohua He
1,*,
Jingguang Chen
2,3,* and
Guoyou Ye
2,4
1
Group of Crop Genetics and Breeding, School of Agriculture Science, Jiangxi Agricultural University, Nanchang 330045, China
2
CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518116, China
3
School of Agriculture, Sun Yat-sen University, Guangzhou 510275, China
4
Rice Breeding Innovation Platform, International Rice Research Institute, DAPO Box 7777, Metro Manila 1301, Philippines
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agriculture 2021, 11(1), 70; https://doi.org/10.3390/agriculture11010070
Submission received: 2 December 2020 / Revised: 11 January 2021 / Accepted: 14 January 2021 / Published: 16 January 2021
(This article belongs to the Special Issue Rice Breeding and Genetics)
Browse Figures
Versions Notes

Abstract

:
Zinc (Zn) is an essential trace element for the growth and development of both humans and plants. Increasing the accumulation of Zn in rice grains is important for the world’s nutrition and health. In this study, we used a multiparent advanced generation intercross (MAGIC) population constructed using four parental lines and genotyped using a 55 K rice SNP array to identify QTLs related to Zn2+ concentrations in shoots at the seedling stage and grains at the mature stage. Five QTLs were detected as being associated with shoot Zn2+ concentration at the seedling stage, which explained 3.7–5.7% of the phenotypic variation. Six QTLs were detected as associated with grain Zn2+ concentration at the mature stage, which explained 5.5–8.9% of the phenotypic variation. Among the QTLs, qSZn2-1/qGZn2 and qSZn3/qGZn3 were identified as being associated with both the shoot and grain contents. Based on gene annotation and literature information, 16 candidate genes were chosen in the regions of qSZn1, qSZn2-1/qGZn2, qSZn3/qGZn3, qGZn7, and qGZn8. Analysis of candidate genes through qRT-PCR, complementation assay using the yeast Zn-uptake-deficient double-mutant ZHY3, and sequencing of the four parental lines suggested that LOC_Os02g06010 may play an important role in Zn2+ accumulation in indica rice.

1. Introduction

Zinc (Zn) is an essential micronutrient for plant growth and development [1]. Zn is also a component of numerous enzymes [2,3]. For instance, alcohol dehydrogenase (ADH), carbonic anhydrase (CA), copper/zinc superoxide dismutase (CSD), and zinc finger domain-containing proteins (ZFPs) are related to Zn in plants [1]. Furthermore, Zn is an essential component of the zinc finger motif found in many DNA-binding transcription regulators (TRs) [4]. Therefore, Zn is necessary for many biochemical processes to function properly, and adequate Zn supply is critical to crop productivity [5]. However, Zn deficiency is the most widely occurring soil micronutrient deficiency worldwide and adversely affects crop production on millions of hectares of arable land, especially in alkaline soil [6].
Zn deficiency in the edible parts of crops is one of the main reasons for Zn deficiency in humans, since the primary source of Zn intake is through food and vegetables. Zn deficiency is related to health problems such as growth retardation, impaired immune function, loss of appetite, diarrhea, hair loss, weight loss, eye and skin lesions, mental lethargy and delayed healing of wounds [7]. It has been estimated that about one-third of the human population, particularly women and children, suffer from Zn-deficiency-related health problems. Rice is one of the major food crops in the world and the main food source for more than half of the world’s population [8]. Most rice-dependent regions are underdeveloped and cannot afford to diversify their meals by adding mineral-rich fruits, vegetables, and meat [9]. The zinc content of polished rice, one of the rice varieties currently popular in the world, is only 12 mg/kg [10]. However, the international recommendation for a human’s daily intake of zinc is 15 mg [11]. Therefore, increasing the zinc content in rice could play an important role in the improvement of human health. Breeding for high grain zinc content is now one of the key objectives of rice breeding programs worldwide. Projects dedicated to crop biofortification all have Zn content as a key component.
To date, many zinc-related absorptions and transport genes in rice have been cloned. These mainly include the P1B-type Heavy Metal ATPase (HMA) and Zinc-regulated transporter iron regulated transporter-like (ZIP)-family proteins. OsHMA2 is mostly expressed in the roots of rice [12] and mediates the transport of zinc from roots to stems [13]. OsHMA3 has been reported to be a transporter of Cd2+ located on the vacuolar membrane [14]. Recently, Cai et al. [15] showed that OsHMA3 plays an important role in rice roots in both Zn detoxification and storage by sequestration into the vacuoles, depending on Zn concentration in the environment. OsZIP1 is involved in the detoxification of the poison of zinc ions in rice [16,17,18]. OsZIP3 is mainly expressed in the nodes and participates in the transport of zinc ions in the shoots of rice [18,19]. OsZIP4 has zinc ion transport activity, and overexpression of the OsZIP4 zinc transporter produces disarrangement of zinc distribution in rice plants [20,21,22]. OsZIP5 is a plasma-membrane zinc transporter in rice [5], and OsZIP5 functions redundantly to OsZIP9 but has a relatively weaker effect [23]. OsZIP7 encodes a plasma-membrane-localized protein with influx transport activity for both Zn and Cd, which plays an integral role in xylem loading in roots and intervascular transfer in nodes to preferentially deliver Zn and Cd to developing tissues and rice grains [24,25,26]. OsZIP9 is localized at the root exodermis and endodermis, and functions as an influx transporter of Zn and contributes to Zn uptake under Zn-limited conditions in rice [23,27,28].
Quantitative trait locus (QTL) identification via linkage mapping and association mapping has been widely used to discover genetic factors controlling agronomic traits, including absorption and transport of metal ions [29,30,31]. Linkage mapping using different biparental populations has been used to identify a series of QTLs for shoot and grain Zn contents [32,33,34]. Association mapping, or genome-wide association study (GWAS), has been used to identify QTLs for grain Fe, Zn, and several other mineral elements [33,35]. Yang et al. [29] identified three QTLs associated with Zn concentration in shoots at the mature stage using a panel of 529 varieties. To date, no QTLs have been fine-mapped or cloned for Zn2+ accumulation in rice. In recent years, multiparent advanced generation intercross (MAGIC) populations have become popular population types for mapping and developing breeding lines with multiple desirable traits [36,37,38,39]. MAGIC populations have a relatively wide genetic background without significant population structure, which is a major constraint in association mapping using diversity panels. In this study, we aimed to use a highly diverse MAGIC population, genotyped with a 55 K single nucleotide polymorphism (SNP) array, to illuminate the genetic basis of Zn accumulation.

2. Materials and Methods

2.1. Plant Materials

The MAGIC population used in this study consisted of 215 lines of the DC1 population developed with four parental lines, A, B, C, and D, from different countries [36]. The sources and agronomic characters of the four parental lines are given in Supplementary Table S1. The rice varieties for haplotype classification of LOC_Os02g06010 were from Liu et al. [40].

2.2. Plant Growth Condition

A pot experiment was carried out in the greenhouse of the Agricultural Genomics Institute in Shenzhen, Chinese Academy of Agricultural Sciences. The soil from a field site was naturally dried, and then crushed and sieved through a 3 mm screen mesh. After mixing evenly, the soil was put in a pot, with 15 kg per pot. Additionally, 1.3 g urea and 0.37 g KH2PO4 were added to each pot as basal fertilizer for N, P, and K supply, respectively. Each pot was then saturated with distilled water and drained to equilibrium for 7 days under natural conditions.
The rice seeds of the MAGIC population lines were disinfected in a 1% sodium hypochlorite solution for 15 min, followed by thorough washing with deionized water. Seeds were then germinated in the dark in water at 30 °C for three days, and the germinated seeds were cultivated in the seedling trays for four weeks. Healthy seedlings were then selected and transferred to the pots with the same culture condition. Six seedlings from a single line were planted in each pot. Throughout the entire growth period, all pots were irrigated with distilled water every day to maintain a water level of 3 cm above the soil surface. Three seedlings were used to collect shoot samples three weeks after transplanting, and grain samples were collected from the remaining three plants at the mature stage. Brown rice samples were taken and peeled. An augmented randomized complete block design with two replicates of 25 lines was used as the experimental layout.

2.3. Measurement of Zinc Concentration

The shoot and grain samples were dried at 65 °C for 3 days. The dried samples were crushed, wet-digested in concentrated HNO3 at 120 °C for 30 min, and further digested with HClO4 at 180 °C until the samples became transparent. The samples were then diluted with ultrapure water. The zinc concentrations were determined by ICP-MS (inductively coupled plasma mass spectrometry).

2.4. SNP Genotyping and Association Analysis

Meng et al. [37] genotyped the MAGIC population with a 55 K SNP array. A three-step filtering strategy was used to select high-quality SNPs for QTL mapping. First, monomorphic markers among the four parents were removed. Second, markers with missing values higher than 10% were deleted. Finally, markers with a minor allele frequency of less than 3% were filtered out. After filtering, 22,160 high-quality markers were kept to be used for analysis.
A mixed linear model (MLM) implemented in TASSEL version 5.2.3 [41] was used to analyze the associations between SNP markers and traits. A value of p < 10−2.5 was used as the threshold to declare the significance of marker–trait associations. R2 was used to evaluate the percentage of phenotypic variance explained.

2.5. RNA Extraction and Real-Time PCR

To examine the expression response of the candidate genes to Zn2+ deficiency, root samples were taken at the three-leaf stage from seedlings of rice variety cv. Nipponbare or the four parental lines grown in 1/4 strength IRRI (International Rice Research Institute) solution with 0.77 μM Zn2+ or without Zn2+. A randomized complete block design with three replicates and a plot size of 24 seedlings were used as the experimental layout. Samples were taken from all 24 seedlings in each plot and mixed for RNA extraction. The composition of the full-strength IRRI solution was as follows: 1.0 mM MgSO4·7H2O, 1.25 mM NH4NO3, 0.3 mM KH2PO4, 1.0 mM CaCl2, 0.35 mM K2SO4, 0.5 mM Na2SiO3, 20.0 μM Fe-EDTA, 20.0 μM H3BO3, 9.0 μM MnCl2, 0.32 μM CuSO4, and 0.39 μM (NH4)6Mo7O24, pH 5.5. All plants were grown in a greenhouse under 14 h light (30 °C)/10 h dark (25 °C), at a humidity of 60%.
To investigate the expression pattern of LOC_Os02g06010 in different organs at different growth stages, the 3 week old seedlings of cv. Nipponbare precultured hydroponically were transplanted to the paddy field in the Shenzhen Experimental Farm of the Agricultural Genomics Institute in Shenzhen, Chinese Academy of Agricultural Sciences. Tissue samples collected included roots, basal stem, leaf sheath, and leaf blade at the vegetative stage, and roots, basal stem, lower leaf sheath, lower leaf blade, flag leaf sheath, flag leaf blade, node I, node II, inter node II, peduncle, rachis, spikelet, husk, and seed at the reproductive stages. A single plant was regarded as a biological replicate and three biological replicates were used.
The total RNA was extracted by Trizol (Vazyme Biotech Co. Ltd., Nanjing, China). The total RNA was then reverse-transcribed using the HiScript Q RT SuperMix for qPCR kit (Vazyme Biotech Co. Ltd., Nanjing, China). The AceQ Universal SYBR qPCR Master Mix kit (Vazyme Biotech Co. Ltd., Nanjing, China) was used for quantitative analysis [42]. The primers used for qRT-PCR are given in Supplementary Table S2.

2.6. Expression of Candidate Genes in Yeast

The Zn2+ translocation ability of each candidate gene protein was examined via a yeast complementation assay. The yeast strain used was the Zn-uptake-deficient double-mutant ZHY3 (MATα ade6 can1 his3 leu2 trp1 ura3 zrt1::LEU2 zrt2::HIS3). The open reading frames of all candidate genes were amplified from the full-length cDNA of rice cv. Nipponbare, and the primer sequences used are shown in Supplementary Table S3. Each candidate gene was ligated into a pYES2 vector with the correct direction.
Using a yeast transformation kit (Coolaber Technology Co. Ltd., Beijing, China), the empty vector pYES2, positive control OsZIP5, or candidate gene vector was introduced into the ZHY3 yeast cells. The transformed yeast was selected on a SD medium without uracil (SD-U). Positive clones were cultured in the SD-U liquid medium until the early logarithmic phase, concentrated, and washed three times with sterile ultrapure water. The yeast cell suspensions of ZHY3 transformed with empty vector pYES2, positive control OsZIP5, or candidate genes were serially diluted (1:10), and 6 μL of the cell suspension was spotted onto solid media containing 1, 3, or 100 μmol/L ZnSO4. The growth phenotypes were evaluated by culturing the plates at 30 °C for three days.
The OD600 of the overnight yeast cells in the SD-U liquid medium was adjusted to 1.0 with sterile distilled water. Subsequently, 20 μL cell suspensions were added to 20 mL liquid SD-U media containing 3 or 100 μmol/L ZnSO4 in each bottle. The OD600 was determined at the indicated time.

2.7. Sequence Analysis of LOC_Os02g06010 in Four Parental Lines and 30 Other Rice Varieties

DNA samples of the four parental lines were extracted from the seedling samples by CTAB. The DNA samples were then used as templates to amplify the full-length genomic sequence and promoter of LOC_Os02g06010 via the KOD-FX polymerase (Toyobo, Japan) using specific primers (Supplementary Table S4). The PCR products were sequenced by Sangon Biotech Co., Ltd. (Shanghai, China). LOC_Os02g06010 sequences of the four parental lines were aligned and analyzed using DNAMAN.
The accessions were genotyped using the Illumina HiSeq 2000 (PE150) (50X) by Berry Genomics Corporation; the average sequencing depth of each accession genome was 50× [40]. The sequence of LOC_Os02g06010 in different rice varieties was analyzed according to the sequencing data.

3. Results

3.1. Distribution of Zn2+ Concentration in Shoots and Grains of MAGIC Population

We evaluated the Zn2+ accumulation in the 215 lines of the MAGIC population and its four parental lines. The zinc concentrations in shoots of the vegetative growth period (3 weeks after being transferred) and brown rice at the mature stage were determined, respectively. The Zn2+ concentrations in shoots and brown rice were highest in the parental line B, and the lowest in the parental line D (Figure 1A,B). The MAGIC population exhibited significant phenotypic variation in Zn2+ concentrations in shoots and brown rice. The distributions of the two traits were approximately normal (Figure 1A,B).
The correlation between Zn concentration in the shoots of seedlings and concentration in the mature brown rice was positive and moderate (correlation coefficient was 0.39; Figure 2).

3.2. Mapping of QTLs Associated with Zn2+ Concentration in Shoots and Grains

Five QTLs associated with Zn2+ concentration in shoots were located on Chromosomes 1, 2, 3, and 8. They explained 4.5% (qSZn1), 5.3% (qSZn2-1), 3.7% (qSZn2-2), 3.9% (qSZn3), and 5.7% (qSZn8) of the phenotypic variation, respectively (Table 1, Figure 3). Six QTLs were identified as being associated Zn2+ concentration in brown rice, which were distributed on Chromosomes 2, 3, 5, 7 and 8, and explained 5.7% (qGZn2), 5.8% (qGZn3), 5.5% (qGZn5-1), 5.5% (qGZn5-2), 6.1% (qGZn7), and 8.9% (qGZn8) of phenotypic variation, respectively (Table 1, Figure 3). qSZn2-1 was co-located with qGZn2, and qSZn3 was co-located with qGZn3. The qSZn2-2 and qGZn5-1 regions covered the genes OsYSL14 and OsZIP7, respectively (Table 1).

3.3. Identification of Candidate Genes Related to Zn2+ Accumulation in Shoots and Grains

The QTLs qSZn1, qSZn2-1/qGZn2, qSZn3/qGZn3, qGZn7, and qGZn8 were relatively more relevant for Zn2+ concentration in shoots, concentration in brown rice, or both (Table 1, Figure 3). Based on annotation (http://rice.plantbiology.msu.edu/index.shtml) and literature information, 16 genes were chosen as the candidate genes responsible for Zn2+ accumulation (Table 2). Among them, 15 encode proteins with transmembrane structure (Supplementary Figure S1). Six genes, namely LOC_Os02g06010 (qSZn2-1/qGZn2), LOC_Os07g37510 (qGZn7), LOC_Os08g41370, LOC_Os08g42020, LOC_Os08g42150, and LOC_Os08g42170 (qGZn8) were found to be induced by Zn2+ deficiency (Figure 4).
To determine the Zn2+ transport activity of the expressed proteins of six candidate genes, expression levels of the candidate genes in the yeast mutant strain ZHY3 were compared with the empty vector pYES2 and positive control OsZIP5. In terms of growth, there were no significant differences between the ZHY3 transformed by the six candidate genes, OsZIP5 and pYES2 under the control medium and the solid medium containing 100 μM Zn2+ (Supplementary Figure S2). There was no significant difference between the LOC_Os07g37510-, LOC_Os08g41370-, LOC_Os08g42020-, LOC_Os08g42150-, LOC_Os08g42170-, and pYES2-transformed ZHY3 on the solid medium containing 1 μM or 3 μM Zn2+ either (Supplementary Figure S2), suggesting that these proteins may not have Zn2+ transport activity. The growth of LOC_Os02g06010-transformed ZHY3 was significantly higher than that of pYES2-transformed ZHY3 in the solid medium containing 1 μM or 3 μM Zn2+ after 3 days (Supplementary Figure S2; Figure 5A), suggesting that LOC_Os02g06010 may have Zn2+ transport activity. Experiment using a liquid medium with different Zn2+ concentrations confirmed that the growth of ZHY3 under 3 μM Zn2+ supply was increased by LOC_Os02g06010 (Figure 5A,B).

3.4. Expression Pattern and Sequence Analysis of LOC_Os02g06010 in Parental Lines

The difference of LOC_Os02g06010 in the four parental lines was amplified by PCR and sequenced. The coding region sequence of LOC_Os02g06010 showed no difference between the four parental lines (Figure 6A). The sequence in the 2000 bp region of the upstream of the ATG and the 3’-UTR sequence after the TGA of LOC_Os02g06010 were identical between A, B, and C (Figure 6A). However, D had four single-nucleotide mutations in the first intron, three single-nucleotide mutations in the second intron, and one single-nucleotide mutation in the 3’-UTR (Figure 6A).
The sequence of LOC_Os02g06010 in different rice varieties was analyzed according to the sequencing data, and SNP: −951 bp and SNP: −830 bp in the intron region of the upstream of the ATG were found in the accessions. The two SNPs of most accessions were the same as those of A, B, and C (ABC-TYPE), and only seven accessions were the same as D (D-TYPE). We selected 30 accessions of ABC-TYPE and seven accessions of D-TYPE to measure the Zn2+ concentration in brown rice (Supplementary Table S5). It was found that the Zn2+ concentration in brown rice of ABC-TYPE was 13.1% higher than that of D-TYPE (Supplementary Figure S3).
The expression pattern of LOC_Os02g06010 in response to Zn2+ deficiency was further analyzed in four parental lines. Compared with the normal Zn2+ supply, the expression of LOC_Os02g06010 was significantly increased by Zn2+ deficiency in A, B, and C, while it was significantly reduced in D (Figure 6B).

3.5. Expression of LOC_Os02g06010 in Different Organs at Different Growth Stages

To further analyze the biological functions of LOC_Os02g06010, its expression levels in different organs at different stages were investigated. LOC_Os02g06010 was expressed in all the measured tissues at different growth stages, and was strongly expressed at the flowering stage (Figure 7).

4. Discussion

A set of rice MAGIC populations has been developed by IRRI to better integrate QTL discovery and breeding. Their use in QTL mapping for a range of agronomic traits has been previously reported [36,37,38,39,44]. We previously identified QTLs associated with the toxicity tolerance of rice to three essential metals (Fe, Zn, and Al) by using three of the MAGIC populations, including DC1, DC2, and eight-way populations, genotyped using a 55 K SNP array [37]. In this study, we found that the four parents of the DC1 population displayed substantial differences in Zn2+ concentrations in shoots at the seedling stage and in grains at the mature stage (Figure 1A,B). GWAS was conducted using a mixed linear model for the two traits.
Five QTLs for the concentration of Zn2+ in shoots at the seedling stage, and six QTLs for the concentration of Zn2+ in brown rice at the mature stage were identified (Table 1). These QTLs did not co-locate with those QTLs for Zn2+ concentration in leaves or shoots at the mature stage reported by Norton et al. [35] and Yang et al. [29]. This surprising inconsistency may be attributed to the obvious differences in genetic populations, growth conditions, and growth stages for sample collection. However, QTLs qSZn2-2 and qGZn5-1 identified in this study are physically very close to the known zinc-affecting genes (Table 1), suggesting that our study was of good quality. Functional analysis of some YSL members has shown that they are not only involved in Fe uptake but also in the transport and homeostasis of other transition metals such as Zn2+ [45]. The expression level of OsYSL14 in flag leaves exhibited significant correlations with Fe and/or Zn concentrations in the seeds [43]. OsZIP7 expression in Arabidopsis resulted in a 25% increase in shoot Zn concentrations compared to nontransformed plants [25]. OsZIP7 functions in xylem loading in roots and intervascular transfer in nodes to deliver Zn to the grain in rice [24]. Two QTLs for the two traits were co-located; qSZn2-1 was co-located with qGZn2 and qSZn3 was co-located with qGZn3. This could explain the moderate and positive correlation observed between the two traits (Figure 2).
Screening of 16 candidate genes for five important QTLs, chosen based on annotation and literature information for response to Zn2+ deficiency, identified six responsive genes (Figure 4). Among them, only LOC_Os02g06010 was found to have Zn2+ transport activity, using the transformation of the yeast mutant strain ZHY3. In rice, many genes such as OsZIP1, OsZIP3, OsZIP5, and OsZIP9 have been proven to have Zn2+ transport activity in yeast [23,27,46]. LOC_Os02g06010 codes for an integral membrane protein, with a domain of unknown function domain (DUF) and transmembrane structure (Supplementary Figure S1).
Further functional analysis of LOC_Os02g06010 in four parental lines showed that the expression of LOC_Os02g06010 was significantly induced in A, B, and C but inhibited in D under zinc-deficient conditions (Figure 6B). The different expression patterns of LOC_Os02g06010 in the parental lines may have caused the difference in zinc accumulation among the four parental lines. To find the reasons for the different expression levels of the four parental lines, we analyzed the promoter sequence and genome sequence of LOC_Os02g06010. The coding region of LOC_Os02g06010 showed no difference between the four parental lines (Figure 6A). In the 3’-UTR and the introns, parental lines A, B, and C were identical, while the parental line D showed one and seven single-nucleotide mutations, respectively. (Figure 6A). The UTR region does not encode amino acids, but plays an important role in the regulation of gene expression. In post-transcriptional regulation, the noncoding regions of mRNA (including 5’-UTR and 3’-UTR, especially 3’-UTR) determine the translation speed, stability, subcellular localization, degradation, and other features of mRNA [47,48,49]. The mutation of the 3’-UTR in the rice OsCKX2-2 gene can affect CKX enzyme activity to regulate rice development and grain size [50]. Intron retention, as a component of regulated gene expression programs, could increase transcription levels by affecting the rate of transcription, nuclear export, and transcript stability [51,52]. An intron was converted into one that increased mRNA accumulation 24-fold and reporter enzyme activity 40-fold relative to the intronless control by introducing 11 copies of the more active TTNGATYTG motif in Arabidopsis [53]. An intron-derived motif strongly increased gene expression from transcribed sequences via a splicing independent mechanism in Arabidopsis thaliana [54]. An intron-containing plasmid increased the level of GUS enzyme activity 10- to 40-fold and 80- to 90-fold compared with the intronless plasmid, pBI221, in transgenic rice protoplasts and transgenic rice tissues, respectively [55]. The intron elevated GUS gene expression mainly at the mRNA accumulation level, but also stimulated enhancement at the translational level in rice [56]. The 5’ UTR intron of the rice rubi3 gene enhanced GUS reporter gene activity in transgenic lines about 29-fold [57]. Wu et al. [58] found that the addition of the maize Ubi intron 1 significantly enhanced the OsMT2b promoter activity in rice embryos, suggesting that the natural variations observed in the introns or UTRs may cause differences in expression. SNP: −951 bp and SNP: −830 bp were found in the sequenced varieties, and the Zn2+ concentration in brown rice of ABC-TYPE was higher than that of D-TYPE (Supplementary Table S5; Supplementary Figure S3). It is speculated that SNP: −951 bp and SNP: −830 bp in the first intron region of the upstream of the ATG may be the key sites for regulating LOC_Os02g06010 expression. LOC_Os02g06010 was expressed in all the measured tissues of different growth stages of rice, and was more strongly expressed at the flowering stage (Figure 7). Taken together, the present results suggest that LOC_Os02g06010 may play an important role in Zn2+ accumulation in indica rice. Further characterization of LOC_Os02g06010 is needed to elucidate its functional significance in Zn2+ uptake, translocation, and accumulation in rice.

5. Conclusions

In this study, we used a MAGIC population to locate QTLs related to Zn2+ concentration in shoots at the seedling stage and in grains at the mature stage. Five QTLs (qSZn1, qSZn2-1, qSZn2-2, qSZn3, and qSZn8) were detected to be relevant for Zn2+ concentration in shoots at the seedling stage, and explained 3.7–5.7% of the phenotypic variation. Six QTLs (qGZn2, qGZn3, qGZn5-1, qGZn5-2, qGZn7, and qGZn8) were detected to be relevant for Zn2+ concentration in grains at the mature stage, and explained 5.5–8.9% of the phenotypic variation. Sixteen candidate genes were predicted, and LOC_Os02g06010 was selected as our candidate gene through qRT-PCR and yeast heterologous functional complementation verification test. The expression of LOC_Os02g06010 was significantly induced in A, B, and C, but inhibited in D by Zn2+ deficiency. The coding region of LOC_Os02g06010 showed no difference between the four parental lines. In the 3’-UTR and introns, parental lines A, B, and C were identical, while the parental line D showed one and seven single-nucleotide mutations, respectively. SNP: −951 bp and SNP: −830 bp in the first intron may be the key sites for regulating LOC_Os02g06010 expression. LOC_Os02g06010 was strongly expressed at the flowering stage of rice. These results suggest that LOC_Os02g06010 may play important roles in Zn2+ accumulation in indica rice.

Supplementary Materials

The following are available online at https://www.mdpi.com/2077-0472/11/1/70/s1, Table S1: Description of the four parental lines used for developing the MAGIC DC1 population, Table S2: Primers used for qRT-PCR, Table S3: Primers used to amplify the open reading frames of six candidate genes and OsZIP5, Table S4: Primers used to amplify the full length of LOC_Os02g06010 and its promoter, Table S5: Detailed information of 37 rice accessions, Figure S1: Predicted topological models of proteins encoded by 16 candidate genes, Figure S2: Complementation of the yeast mutant strain ZHY3 by OsZIP5 and six candidate genes, Figure S3: Haplotype analyses of LOC_Os02g06010.

Author Contributions

Conceived and designed the experiments: G.Y., J.C., and H.H. Performed the experiments: S.L., W.Z., J.C., X.L. and J.B. Analyzed the data: J.C., S.L. Wrote and revised the paper: S.L., J.C., and G.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was financially supported by the Agricultural Science and Technology Innovation Program Cooperation and Innovation Mission (CAAS-XTCX2016001), Shenzhen Science and Technology Program (JCYJ20190813104211014) and the Dapeng District Industry Development Special Funds (KY20180218).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data is contained within the article or Supplementary Material.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Broadley, M.; Brown, P.; Cakmak, I.; Rengel, Z.; Zhao, F. Function of nutrients. In Marschners Mineral Nutrition of Higher Plants; Academic Press: Cambridge, MA, USA, 2012; pp. 191–248. [Google Scholar]
  2. Wissuwa, M.; Ismail, A.M.; Yanagihara, S. Effects of zinc deficiency on rice growth and genetic factors contributing to tolerance. Plant Physiol. 2006, 142, 731–741. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Bandyopadhyay, T.; Mehra, P.; Hairat, S.; Giri, J. Morpho-physiological and transcriptome profiling reveal novel zinc deficiency-responsive genes in rice. Funct. Integr. Genom. 2017, 17, 565–581. [Google Scholar] [CrossRef] [PubMed]
  4. Figueiredo, D.D.; Barros, P.M.; Cordeiro, A.M.; Serra, T.S.; Lourenco, T.; Chander, S.; Oliveira, M.M.; Saibo, N.J. Seven zinc-finger transcription factors are novel regulators of the stress responsive gene OsDREB1B. J. Exp. Bot. 2012, 63, 3643. [Google Scholar] [CrossRef] [Green Version]
  5. Lee, S.; Jeong, H.J.; Kim, S.A.; Lee, J.; Guerinot, M.L.; An, G. OsZIP5 is a plasma membrane zinc transporter in rice. Plant Mol. Biol. 2010, 73, 507–517. [Google Scholar] [CrossRef] [PubMed]
  6. Barker, A.; Pilbeam, D. Handbook of Plant Nutrition; CRC Press: Boca Raton, FL, USA, 2015. [Google Scholar]
  7. Wang, L.C.; Busbey, S.; Bushey, S. Images in clinical medicine. Acquired acrodermatitis enteropathica. New Engl. J. Med. 2005, 352, 1121. [Google Scholar] [CrossRef]
  8. Zuo, J.; Li, J. Molecular genetic dissection of quantitative trait loci regulating rice grain size. Annu. Rev. Genet. 2014, 48, 99–118. [Google Scholar] [CrossRef]
  9. Shahzad, Z.; Rouached, H.; Rakha, A. Combating mineral malnutrition through iron and zinc biofortification of cereals. Compr. Rev. Food Sci. Food Saf. 2014, 13, 329–346. [Google Scholar] [CrossRef]
  10. Bouis, H.E.; Welch, R.M. Biofortification—A sustainable agricultural strategy for reducing micronutrient malnutrition in the global south. Crop Sci. 2010, 50, 20–32. [Google Scholar] [CrossRef] [Green Version]
  11. Patterson, K.Y.; Holbrook, J.T.; Bodner, J.E.; Kelsay, J.L.; Smith, J.C.; Veillon, C. Zinc, copper, and manganese intake and balance for adults consuming self-selected diets. Am. J. Clin. Nutr. 1984, 40, 1397–1403. [Google Scholar] [CrossRef] [Green Version]
  12. Takahashi, R.; Ishimaru, Y.; Shimo, H.; Ogo, Y.; Nakanishi, H. The OsHMA2 transporter is involved in root-to-shoot translocation of Zn and Cd in rice. Plant Cell Environ. 2012, 35, 1948–1957. [Google Scholar] [CrossRef]
  13. Yamaji, N.; Xia, J.; Mitani-Ueno, N.; Yokosho, K.; Ma, J.F. Preferential delivery of zinc to developing tissues in rice is mediated by P-type heavy metal ATPase OsHMA2. Plant Physiol. 2013, 162, 927–939. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Miyadate, H.; Adachi, S.; Hiraizumi, A.; Tezuka, K.; Nakazawa, N.; Kawamoto, T.; Katou, K.; Kodama, I.; Sakurai, K.; Takahashi, H.; et al. OsHMA3, a P1B-type of ATPase affects root-to-shoot cadmium translocation in rice by mediating efflux into vacuoles. New Phytol. 2010, 189, 190. [Google Scholar] [CrossRef] [PubMed]
  15. Hongmei, C.; Sheng, H.; Jing, C.; Naoki, Y.; Ma, J.F. The tonoplast-localized transporter OsHMA3 plays an important role in maintaining Zn homeostasis in rice. J. Exp. Bot. 2019, 70, 2717–2725. [Google Scholar]
  16. Ramegowda, Y.; Venkategowda, R.; Jagadish, P.; Govind, G.; Hanumanthareddy, R.R.; Makarla, U.; Guligowda, S.A. Expression of a rice Zn transporter, OsZIP1, increases Zn concentration in tobacco and finger millet transgenic plants. Plant Biotechnol. Rep. 2013, 7, 309–319. [Google Scholar] [CrossRef]
  17. Liu, X.S.; Feng, S.J.; Zhang, B.Q.; Wang, M.Q.; Yang, Z.M. OsZIP1 functions as a metal efflux transporter limiting excess zinc, copper and cadmium accumulation in rice. BMC Plant Biol. 2019, 19, 283. [Google Scholar] [CrossRef] [PubMed]
  18. Chen, W.R.; Feng, Y.; Chao, Y.E. Genomic analysis and expression pattern of OsZIP1, OsZIP3, and OsZIP4 in two rice (Oryza sativa L.) genotypes with different zinc efficiency. Russ. J. Plant Physiol. 2008, 55, 400–409. [Google Scholar] [CrossRef]
  19. Sasaki, A.; Yamaji, N.; Mitani-Ueno, N.; Kashino, M.; Ma, J.F. A node-localized transporter OsZIP3 is responsible for the preferential distribution of Zn to developing tissues in rice. Plant J. 2015, 84, 374–384. [Google Scholar] [CrossRef] [Green Version]
  20. Ishimaru, Y.; Suzuki, M.; Kobayashi, T.; Takahashi, M.; Nakanishi, H.; Mori, S.; Nishizawa, N.K. OsZIP4, a novel zinc-regulated zinc transporter in rice. J. Exp. Bot. 2005, 56, 3207. [Google Scholar] [CrossRef]
  21. Ishimaru, Y.; Masuda, H.; Suzuki, M.; Bashir, K.; Takahashi, M.; Nakanishi, H.; Mori, S.; Nishizawa, N.K. Overexpression of the OsZIP4 zinc transporter confers disarrangement of zinc distribution in rice plants. J. Exp. Bot. 2007, 58, 2909. [Google Scholar] [CrossRef] [Green Version]
  22. Youngsup, S.; Ryuichi, T.; Hiromi, N.; Takashi, Y. Sweet potato expressing the rice Zn transporter OsZIP4 exhibits high Zn content in the tuber. Plant Biotechnol. 2016, 33, 99–104. [Google Scholar]
  23. Tan, L.; Qu, M.; Zhu, Y.; Peng, C.; Wang, J.; Gao, D.; Chen, C. ZINC TRANSPORTER5 and ZINC TRANSPORTER9 function synergistically in Zinc/Cadmium uptake. Plant Physiol. 2020, 183, 1235–1249. [Google Scholar] [CrossRef] [PubMed]
  24. Tan, L.; Zhu, Y.; Fan, T.; Peng, C.; Wang, J.; Sun, L.; Chen, C. OsZIP7 functions in xylem loading in roots and inter-vascular transfer in nodes to deliver Zn/Cd to grain in rice. Biochem. Biophys. Res. Commun. 2019, 512, 112–118. [Google Scholar] [CrossRef] [PubMed]
  25. Ricachenevsky, F.K.; Punshon, T.; Lee, S.; Oliveira, B.H.N.; Guerinot, M.L. Elemental profiling of rice FOX lines leads to characterization of a new Zn plasma membrane transporter, OsZIP7. Front. Plant Sci. 2018, 9, 856. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Gindri, R.G.; Navarro, B.B.; da Cruz Dias, P.V.; Tarouco, C.P.; Ricachenevsky, F.K. Physiological responses of rice (Oryza sativa L.) oszip7 loss-of-function plants exposed to varying Zn concentrations. Physiol. Mol. Biol. Plants 2020, 26, 1349–1359. [Google Scholar] [CrossRef]
  27. Yang, M.; Li, Y.; Liu, Z.; Tian, J.; Lian, X. A high activity zinc transporter OsZIP9 mediates zinc uptake in rice. Plant J. 2020, 103, 1695–1709. [Google Scholar] [CrossRef]
  28. Huang, S.; Sasaki, A.; Yamaji, N.; Okada, H.; Mitaniueno, N.; Ma, J.F. The ZIP transporter family member OsZIP9 contributes to root Zn uptake in rice under Zn-limited conditions. Plant Physiol. 2020, 183, 1224–1234. [Google Scholar] [CrossRef]
  29. Yang, M.; Lu, K.; Zhao, F.-J.; Xie, W.; Ramakrishna, P.; Wang, G.; Du, Q.; Liang, L.; Sun, C.; Zhao, H.; et al. Genome-wide association studies reveal the genetic basis of ionomic variation in rice. Plant Cell 2018, 30, 2720–2740. [Google Scholar] [CrossRef] [Green Version]
  30. Gao, Z.; Wang, Y.; Chen, G.; Zhang, A.; Qian, Q. The indica nitrate reductase gene OsNR2 allele enhances rice yield potential and nitrogen use efficiency. Nat. Commun. 2019, 10. [Google Scholar] [CrossRef]
  31. Yan, H.; Xu, W.; Xie, J.; Gao, Y.; He, Z. Variation of a major facilitator superfamily gene contributes to differential cadmium accumulation between rice subspecies. Nat. Commun. 2019, 10, 2562. [Google Scholar] [CrossRef] [Green Version]
  32. Agarwal, S.; Vgn, T.V.; Kotla, A.; Mangrauthia, S.K.; Neelamraju, S. Expression patterns of QTL based and other candidate genes in Madhukar × Swarna RILs with contrasting levels of iron and zinc in unpolished rice grains. Gene 2014, 546, 430–436. [Google Scholar] [CrossRef]
  33. Zhang, M.; Pinson, S.R.M.; Tarpley, L.; Huang, X.-Y.; Salt, D.E. Mapping and validation of quantitative trait loci associated with concentrations of 16 elements in unmilled rice grain. Theor. Appl. Genet. 2014, 127, 137–165. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Swamy, B.P.M.; Kaladhar, K.; Anuradha, K.; Batchu, A.K.; Longvah, T.; Sarla, N. QTL Analysis for grain Iron and Zinc concentrations in two O. nivara derived backcross populations. Rice Sci. 2018, 25, 197–207. [Google Scholar] [CrossRef]
  35. Norton, G.J.; Deacon, C.M.; Xiong, L.; Huang, S.; Meharg, A.A.; Price, A.H. Genetic mapping of the rice ionome in leaves and grain: Identification of QTLs for 17 elements including arsenic, cadmium, iron and selenium. Plant Soil 2010, 329, 139–153. [Google Scholar] [CrossRef]
  36. Meng, L.; Guo, L.; Ponce, K.; Zhao, X.; Ye, G. Characterization of three rice multiparent advanced generation intercross (MAGIC) populations for quantitative trait loci identification. Plant Genome 2016, 9. [Google Scholar] [CrossRef] [Green Version]
  37. Meng, L.; Wang, B.; Zhao, X.; Ponce, K.; Qian, Q.; Ye, G. Association mapping of Ferrous, Zinc, and Aluminum tolerance at the seedling stage in indica rice using MAGIC populations. Front. Plant Sci. 2017, 8, 1822. [Google Scholar] [CrossRef] [Green Version]
  38. Zhang, Y.; Ponce, K.; Meng, L.; Chakraborty, P.; Ye, G. QTL identification for salt tolerance related traits at the seedling stage in indica rice using a multi-parent advanced generation intercross (MAGIC) population. Plant Growth Regul. 2020, 92, 365–373. [Google Scholar] [CrossRef]
  39. Ponce, K.; Zhang, Y.; Guo, L.; Leng, Y.; Ye, G. Genome-wide association study of grain size traits in indica rice multiparent advanced generation intercross (MAGIC) population. Front. Plant Sci. 2020, 11, 395. [Google Scholar] [CrossRef] [Green Version]
  40. Liu, H.; Zhan, J.; Li, J.; Lu, X.; Liu, J.; Wang, Y.; Zhao, Q.; Ye, G. Genome-wide association study (GWAS) for mesocotyl elongation in rice (Oryza sativa L.) under multiple culture conditions. Genes 2020, 11, 49. [Google Scholar] [CrossRef] [Green Version]
  41. Bradbury, P.J.; Zhang, Z.; Kroon, D.E.; Casstevens, T.M.; Ramdoss, Y.; Buckler, E.S. TASSEL: Software for association mapping of complex traits in diverse samples. Bioinformatics 2007, 23, 2633–2635. [Google Scholar] [CrossRef]
  42. Chen, J.; Qi, T.; Hu, Z.; Fan, X.; Zhu, L.; Iqbal, M.F.; Yin, X.; Xu, G.; Fan, X. OsNAR2.1 positively regulates drought tolerance and grain yield under drought stress conditions in rice. Front. Plant Sci. 2019, 10, 197. [Google Scholar] [CrossRef] [Green Version]
  43. Sperotto, R.A.; Boff, T.; Duarte, G.L.; Santos, L.S.; Grusak, M.A.; Fett, J.P. Identification of putative target genes to manipulate Fe and Zn concentrations in rice grains. J. Plant Physiol. 2010, 167, 1500–1506. [Google Scholar] [CrossRef] [PubMed]
  44. Bandillo, N.; Raghavan, C.; Muyco, P.A.; Sevilla, M.A.L. Multi-parent advanced generation inter-cross (MAGIC) populations in rice: Progress and potential for genetics research and breeding. Rice 2013, 6, 1–15. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Catherine, C.; Cassin, G.; Couch, D.; Divol, F.; Higuchi, K.; Le Jean, M.; Misson, J.; Schikora, A.; Czernic, P.; Mari, S. Metal movement within the plant: Contribution of nicotianamine and yellow stripe 1-like transporters. Ann. Bot. 2009, 103, 1–11. [Google Scholar]
  46. Ramesh, S.A.; Shin, R.; Eide, D.J.; Schachtman, D.P. Differential metal selectivity and gene expression of two zinc transporters from rice. Plant Physiol. 2003, 133, 126–134. [Google Scholar] [CrossRef] [Green Version]
  47. Bashirullah, A.; Cooperstock, R.L.; Lipshitz, H.D. Spatial and temporal control of RNA stability. Proc. Natl. Acad. Sci. USA 2001, 98, 7025–7028. [Google Scholar] [CrossRef] [Green Version]
  48. Mazumder, B.; Seshadri, V.; Fox, P.L. Translational control by the 3′-UTR: The ends specify the means. Trends Biochem. Sci. 2003, 28, 91–98. [Google Scholar] [CrossRef]
  49. Venturin, M.; Moncini, S.; Villa, V.; Russo, S.; Bonati, M.T.; Larizza, L.; Riva, P. Mutations and novel polymorphisms in coding regions and UTRs of CDK5R1 and OMG genes in patients with non-syndromic mental retardation. Neurogenetics 2006, 7, 59–66. [Google Scholar] [CrossRef]
  50. Tsago, Y.; Chen, Z.; Cao, H.; Sunusi, M.; Khan, A.U.; Shi, C.; Jin, X. Rice gene, OsCKX2-2, regulates inflorescence and grain size by increasing endogenous cytokinin content. Plant Growth Regul. 2020, 92, 283–294. [Google Scholar] [CrossRef]
  51. Jacob, A.G.; Smith, C.W.J. Intron retention as a component of regulated gene expression programs. Hum. Genet. 2017, 136, 1043–1057. [Google Scholar] [CrossRef] [Green Version]
  52. Shaul, O. How introns enhance gene expression. Int. J. Biochem. Cell Biol. 2017, 91, 145–155. [Google Scholar] [CrossRef]
  53. Rose, A.B.; Carter, A.; Korf, I.; Kojima, N. Intron sequences that stimulate gene expression in Arabidopsis. Plant Mol. Biol. 2016, 92, 1–10. [Google Scholar] [CrossRef] [PubMed]
  54. Gallegos, J.E.; Rose, A.B. An intron-derived motif strongly increases gene expression from transcribed sequences through a splicing independent mechanism in Arabidopsis thaliana. Sci. Rep. 2019, 9, 1–9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Akira, T.; Satoru, M.; Shozo, O.; Junko, K.; Ko, S.; Kenzo, N. Enhancement of foreign gene expression by a dicot intron in rice but not in tobacco is correlated with an increased level of mRNA and an efficient splicing of the intron. Nucleic Acids Res. 1990, 18, 6767–6770. [Google Scholar]
  56. Lu, J.; Sivamani, E.; Azhakanandam, K.; Samadder, P.; Li, X.; Qu, R. Gene expression enhancement mediated by the 5′ UTR intron of the rice rubi3 gene varied remarkably among tissues in transgenic rice plants. Mol. Genet. Genom. 2008, 279, 563–572. [Google Scholar] [CrossRef] [PubMed]
  57. Samadder, P.; Sivamani, E.; Lu, J.; Li, X.; Qu, R. Transcriptional and post-transcriptional enhancement of gene expression by the 5′ UTR intron of rice rubi3 gene in transgenic rice cells. Mol. Genet. Genom. 2008, 279, 429–439. [Google Scholar] [CrossRef]
  58. Wu, C.S.; Chen, D.Y.; Chang, C.F.; Li, M.J.; Hung, K.Y.; Chen, L.J.; Chen, P.W. The promoter and the 5′-untranslated region of rice metallothionein OsMT2b gene are capable of directing high-level gene expression in germinated rice embryos. Plant Cell Rep. 2014, 33, 793–806. [Google Scholar] [CrossRef]
Agriculture 11 00070 g001 550
Figure 1. Distribution of Zn2+ concentration in shoots at the seedling stage and in grains at the mature stage in the DC1 population. A pot experiment was carried out in the greenhouse of the Agricultural Genomics Institute in Shenzhen, Chinese Academy of Agricultural Sciences. The shoot and grain samples were collected at the seedling stage (3 weeks after transplanting) and the mature stage, respectively. Frequency distribution of (A) Zn2+ concentration in shoots at the seedling stage, and (B) Zn2+ concentration in brown rice at the mature stage.
Figure 1. Distribution of Zn2+ concentration in shoots at the seedling stage and in grains at the mature stage in the DC1 population. A pot experiment was carried out in the greenhouse of the Agricultural Genomics Institute in Shenzhen, Chinese Academy of Agricultural Sciences. The shoot and grain samples were collected at the seedling stage (3 weeks after transplanting) and the mature stage, respectively. Frequency distribution of (A) Zn2+ concentration in shoots at the seedling stage, and (B) Zn2+ concentration in brown rice at the mature stage.
Agriculture 11 00070 g001
Agriculture 11 00070 g002 550
Figure 2. The relationship between Zn2+ concentration in shoots at the seedling stage and in grains at the mature stage in the DC1 population.
Figure 2. The relationship between Zn2+ concentration in shoots at the seedling stage and in grains at the mature stage in the DC1 population.
Agriculture 11 00070 g002
Agriculture 11 00070 g003 550
Figure 3. GWAS of Zn2+ concentration in shoots at the seedling stage and in grains at the mature stage in the MAGIC population. Manhattan and quantile–quantile plots of Zn2+ concentration in shoots at the seedling stage (A,B), and Zn2+ concentration in brown rice at the mature stage (C,D).
Figure 3. GWAS of Zn2+ concentration in shoots at the seedling stage and in grains at the mature stage in the MAGIC population. Manhattan and quantile–quantile plots of Zn2+ concentration in shoots at the seedling stage (A,B), and Zn2+ concentration in brown rice at the mature stage (C,D).
Agriculture 11 00070 g003
Agriculture 11 00070 g004 550
Figure 4. Relative expression of 16 candidate genes under different Zn2+ concentrations. The seedlings of rice cv. Nipponbare at the three-leaf stage grown in 1/4 strength IRRI solution with 0.77 μM Zn2+ (CK) or without Zn2+ (−Zn) for three weeks were used. RNA was extracted from rice roots. Values are mean ± SE (n = 3). The different letters above the bars indicate significant difference between the control and treatments at p < 0.01.
Figure 4. Relative expression of 16 candidate genes under different Zn2+ concentrations. The seedlings of rice cv. Nipponbare at the three-leaf stage grown in 1/4 strength IRRI solution with 0.77 μM Zn2+ (CK) or without Zn2+ (−Zn) for three weeks were used. RNA was extracted from rice roots. Values are mean ± SE (n = 3). The different letters above the bars indicate significant difference between the control and treatments at p < 0.01.
Agriculture 11 00070 g004
Agriculture 11 00070 g005 550
Figure 5. Complementation of the yeast mutant strain ZHY3 by LOC_Os02g06010. (A) Overnight yeast cell suspensions of ZHY3 transformed with empty vector pYES2, OsZIP5, or LOC_Os02g06010 were serially diluted (1:10) and spotted onto solid media containing 1, 3 or 100 μmol/L ZnSO4. Pictures were taken after three days growth at 30 °C. Yeast strains were grown in liquid media with (B) 3 μmol/L ZnSO4 or (C) 100 μmol/L ZnSO4 for 65 h. The absorbance at 600 nm (OD600) of cell cultures was measured every 5 h. Values are mean ± SE (n = 3). The asterisks (*) above the bars indicate significant difference between lines at p < 0.01.
Figure 5. Complementation of the yeast mutant strain ZHY3 by LOC_Os02g06010. (A) Overnight yeast cell suspensions of ZHY3 transformed with empty vector pYES2, OsZIP5, or LOC_Os02g06010 were serially diluted (1:10) and spotted onto solid media containing 1, 3 or 100 μmol/L ZnSO4. Pictures were taken after three days growth at 30 °C. Yeast strains were grown in liquid media with (B) 3 μmol/L ZnSO4 or (C) 100 μmol/L ZnSO4 for 65 h. The absorbance at 600 nm (OD600) of cell cultures was measured every 5 h. Values are mean ± SE (n = 3). The asterisks (*) above the bars indicate significant difference between lines at p < 0.01.
Agriculture 11 00070 g005
Agriculture 11 00070 g006 550
Figure 6. Sequence and expression of LOC_Os02g06010 in the four parental lines of the MAGIC population. (A) Gene structure of LOC_Os02g06010 and polymorphism locations (*: SNP). The SNPs are underlined with asterisks. (B) Relative expression of LOC_Os02g06010 in four parental lines under different Zn2+ concentrations. The seedlings of four parental lines at three-leaf stage, grown in the 1/4 strength IRRI solution with 0.77 μM Zn2+ (CK) or without Zn2+ (-Zn) were used. RNA was extracted from rice roots. Values are mean ± SE (n = 3). The different letters above the bars indicate significant difference between the control and treatments at p < 0.01.
Figure 6. Sequence and expression of LOC_Os02g06010 in the four parental lines of the MAGIC population. (A) Gene structure of LOC_Os02g06010 and polymorphism locations (*: SNP). The SNPs are underlined with asterisks. (B) Relative expression of LOC_Os02g06010 in four parental lines under different Zn2+ concentrations. The seedlings of four parental lines at three-leaf stage, grown in the 1/4 strength IRRI solution with 0.77 μM Zn2+ (CK) or without Zn2+ (-Zn) were used. RNA was extracted from rice roots. Values are mean ± SE (n = 3). The different letters above the bars indicate significant difference between the control and treatments at p < 0.01.
Agriculture 11 00070 g006
Agriculture 11 00070 g007 550
Figure 7. Relative expression of LOC_Os02g06010 in different organs at different growth stages. Samples were collected from cv. Nipponbare grown in a paddy field. Values are mean ± SE (n = 3).
Figure 7. Relative expression of LOC_Os02g06010 in different organs at different growth stages. Samples were collected from cv. Nipponbare grown in a paddy field. Values are mean ± SE (n = 3).
Agriculture 11 00070 g007
Table
Table 1. QTLs associated with Zn2+ concentration in shoots at the seedling stage and grains at the mature stage in the MAGIC population.
Table 1. QTLs associated with Zn2+ concentration in shoots at the seedling stage and grains at the mature stage in the MAGIC population.
QTLsChr.Position (bp)p-ValueContributionGene Symbol References
Zn2+ concentration in shoot
qSZn1159115950.0018354.5%
qSZn2-1233123240.0007935.3%
qSZn2-22236498580.0045613.7%OsYSL14[43]
qSZn33353371030.0036973.9%
qSZn8879477020.0005375.7%
Zn2+ concentration in brown rice
qGZn2231788430.0024515.7%
qGZn33354663030.0021265.8%
qGZn5-1575769550.0030995.5%OsZIP7[24,25,26]
qGZn5-25156602810.0030825.5%
qGZn77220507410.0016026.1%
qGZn88262876547.33E-058.9%
Note: Significant SNPs within a physical distance of 1.5 Mb were delineated into a single QTL. Positions of the most significant SNPs are based on rice reference sequence MSU V 7.0. Chr.: Chromosome; Contribution: phenotypic variance explained.
Table
Table 2. Annotations of the selected candidate genes from promising QTLs detected for Zn2+ accumulation.
Table 2. Annotations of the selected candidate genes from promising QTLs detected for Zn2+ accumulation.
MSU IDAnnotation
qSZn1
LOC_Os01g10600aquaporin protein, putative, expressed
LOC_Os01g10990integral membrane protein DUF6 containing protein, expressed
LOC_Os01g11250potassium channel KAT1, putative, expressed
qSZn2-1/qGZn2
LOC_Os02g06010integral membrane protein, putative, expressed
LOC_Os02g06540transporter family protein, putative, expressed
qSZn3/qGZn3
LOC_Os03g62200ammonium transporter protein, putative, expressed
LOC_Os03g62270MATE efflux family protein, putative, expressed
LOC_Os03g62650ion channel DMI1-like, chloroplast precursor, putative, expressed
LOC_Os03g62750inner membrane protein, putative, expressed
qGZn7
LOC_Os07g37320transporter family protein, putative, expressed
LOC_Os07g37454urate anion exchanger, putative, expressed
LOC_Os07g37510organic cation transporter-related, putative, expressed
qGZn8
LOC_Os08g41370amino acid permease family protein, putative, expressed
LOC_Os08g42020zinc ion binding protein, putative, expressed
LOC_Os08g42150zinc transporter 2 precursor, putative, expressed
LOC_Os08g42170zinc transporter 2 precursor, putative, expressed
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Liu, S.; Zou, W.; Lu, X.; Bian, J.; He, H.; Chen, J.; Ye, G. Genome-Wide Association Study Using a Multiparent Advanced Generation Intercross (MAGIC) Population Identified QTLs and Candidate Genes to Predict Shoot and Grain Zinc Contents in Rice. Agriculture 2021, 11, 70. https://doi.org/10.3390/agriculture11010070

AMA Style

Liu S, Zou W, Lu X, Bian J, He H, Chen J, Ye G. Genome-Wide Association Study Using a Multiparent Advanced Generation Intercross (MAGIC) Population Identified QTLs and Candidate Genes to Predict Shoot and Grain Zinc Contents in Rice. Agriculture. 2021; 11(1):70. https://doi.org/10.3390/agriculture11010070

Chicago/Turabian Style

Liu, Shilei, Wenli Zou, Xiang Lu, Jianmin Bian, Haohua He, Jingguang Chen, and Guoyou Ye. 2021. "Genome-Wide Association Study Using a Multiparent Advanced Generation Intercross (MAGIC) Population Identified QTLs and Candidate Genes to Predict Shoot and Grain Zinc Contents in Rice" Agriculture 11, no. 1: 70. https://doi.org/10.3390/agriculture11010070

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop