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Article

Fine Mapping of Five Grain Size QTLs Which Affect Grain Yield and Quality in Rice

by
Yin Zhou
,
Hanyuan Yang
,
Enyu Liu
,
Rongjia Liu
,
Mufid Alam
,
Haozhou Gao
,
Guanjun Gao
,
Qinglu Zhang
,
Yanhua Li
,
Lizhong Xiong
and
Yuqing He
*
National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2024, 25(8), 4149; https://doi.org/10.3390/ijms25084149
Submission received: 23 February 2024 / Revised: 2 April 2024 / Accepted: 3 April 2024 / Published: 9 April 2024
(This article belongs to the Special Issue Molecular Research for Cereal Grain Quality 2.0)
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Abstract

:
Grain size is a quantitative trait with a complex genetic mechanism, characterized by the combination of grain length (GL), grain width (GW), length to width ration (LWR), and grain thickness (GT). In this study, we conducted quantitative trait loci (QTL) analysis to investigate the genetic basis of grain size using BC1F2 and BC1F2:3 populations derived from two indica lines, Guangzhan 63-4S (GZ63-4S) and TGMS29 (core germplasm number W240). A total of twenty-four QTLs for grain size were identified, among which, three QTLs (qGW1, qGW7, and qGW12) controlling GL and two QTLs (qGW5 and qGL9) controlling GW were validated and subsequently fine mapped to regions ranging from 128 kb to 624 kb. Scanning electron microscopic (SEM) analysis and expression analysis revealed that qGW7 influences cell expansion, while qGL9 affects cell division. Conversely, qGW1, qGW5, and qGW12 promoted both cell division and expansion. Furthermore, negative correlations were observed between grain yield and quality for both qGW7 and qGW12. Nevertheless, qGW5 exhibited the potential to enhance quality without compromising yield. Importantly, we identified two promising QTLs, qGW1 and qGL9, which simultaneously improved both grain yield and quality. In summary, our results laid the foundation for cloning these five QTLs and provided valuable resources for breeding rice varieties with high yield and superior quality.
Keywords:
grain size; QTL; yield; quality; rice

1. Introduction

Rice (Oryza sativa L.) is one of the most important food crops worldwide, providing staple food for more than half of the world’s population [1]. Rice yield is largely determined by three major components: grain weight, number of grains per panicle, and number of effective tillers per plant [2]. Among these, grain weight exhibits a strong correlation with grain size [3]. Grain size is a crucial agronomic trait that has a major impact on the market values of rice grain produce [4,5].
Previous studies have uncovered several signaling pathways controlling grain size, including the guanine nucleotide-binding protein (G protein) signal pathway, the ubiquitin–proteasome pathway, the mitogen-activated protein kinase (MAPK) signaling cascade, the transcriptional regulators pathway, and the phytohormone signaling pathway [3,6,7,8]. G proteins have been demonstrated to regulate grain size in rice. GS3 is the first identified major QTL that negatively controls grain length and weight [9,10]. The GS3-3 allele has a longer grain and heavier weight than the GS3-4 allele [11,12]. GS3 reduces grain length by interacting with RGB1 [13]. Recently, the combination of OsMADS1lgy3, dep1, and gs3 simultaneously increased both grain yield and quality [14]. Furthermore, the ubiquitin–proteasome pathway could directly or indirectly regulate grain size. GW2 encodes a RING-type E3 ubiquitin ligase [15] and negatively regulates grain width, weight, and yield [16,17]. gw2.1, a new allele of GW2, affects grain size by causing cell proliferation, which could be used to improve grain yield and appearance in hybrid breeding [18]. WTG1 determines grain size by influencing cell expansion [19]. The combination of npt1 and dep1-1 has the potential to increase grain yield in rice [20]. In addition, the conserved module of the MAPK signaling cascade plays a key role in the regulation of grain size. The OsMKKK10-OsMKK4-OsMAPK6 cascade modulates grain size by promoting cell division [21,22,23,24,25]. OsWRKY53 can be phosphorylated by OsMAPK6, indicating a cascade of OsMKKK10, OsMKK4, OsMAPK6, and OsWRKY53 in grain size regulation [26]. Moreover, the QTL gene GLW7 could increase grain length, thickness, and weight by promoting cell expansion in the spikelet hull [27]. Similarly, GW8 [28,29], GS2 [30,31], GS9 [32], and GL6 [33,34] are all transcriptional regulators that regulate grain size. Additionally, many phytohormones play significant roles in determining grain size. GW5 was identified as a major QTL for grain width and weight [35]. The decreased expression of GW5 causes wide and heavy grains, resulting in improved yield [36]. OsPPKL1 is encoded by a major QTL for grain length, GL3.1 [37,38,39]. GL3.1 regulates grain size through interaction with OsGSK3 [40]. The qgl3 allele could increase grain yield without affecting grain quality [39]. The TGW6 loss-of-function allele enhances grain weight and yield without affecting grain quality [41]. qTGW3 negatively regulates grain size and weight [42,43,44]. A loss-of-function mutation in qTGW3 results in large and heavy grains, suggesting this locus has potential in breeding high-yield rice [44].
So far, numerous grain size QTLs have been identified, among which a few major QTLs have been cloned. However, the genetic basis of grain size is still not well elucidated, and mining novel QTLs for grain size is of great importance to gain a better understanding of regulation mechanisms and provide gene resources for breeding applications. In this study, we mapped QTLs for grain size using BC1F2 and BC1F2:3 populations derived from the cross between GZ63-4S and W240. A total of twenty-four QTLs were identified, among which five major QTLs were confirmed and further fine mapped. Scanning electron microscopic analysis and expression analysis revealed the cytological basis underlying the five major QTLs on grain size. What is more, an investigation of yield- and quality-related traits demonstrated the potential of these QTLs for breeding rice with high yield and superior quality.

2. Results

2.1. Phenotypic Variation and Correlation in the BC1F2 and BC1F2:3 Populations

The BC1F2 and BC1F2:3 populations, as well as the two parents, exhibited great phenotypic variation in grain size. Compared with GZ63-4S, W240 exhibited greater grain size (Table S1). In two years of repeated field trials, GL ranged from 8.64 mm to 10.27 mm in the BC1F2 population and from 8.58 mm to 10.23 mm in the BC1F2:3 population, respectively (Figure 1A). The ranges of GW were 2.37–2.98 mm and 2.43–2.92 mm (Figure 1B), LWR’s were 2.96–3.92 and 3.11–4.04 (Figure 1C), and GT’s were 1.92–2.19 mm and 1.94–2.22 mm (Figure 1D). Notably, all these traits exhibited normal distribution in both years, indicating typical patterns of quantitative variation. The results suggested that both BC1F2 and BC1F2:3 populations met the requirement for QTL mapping.
The correlation coefficients among the four grain traits in two years showed that both GL and GW exhibited weak to moderate correlations with LWR and GT in 2016 or 2017. LWR displayed no and weak correlation with GT in 2016 and 2017, respectively (Figure S1A,B). Moreover, GL16 and GL17, GW16 and GW17, LWR16 and LWR17, and GT16 and GT17 all displayed moderate correlation (Figure S1C–F).

2.2. QTL Mapping and Verification

To identify QTLs associated with GL, GW, GT, and TGW, 177 polymorphic markers were used to screen 327 plants from the BC1F2 population. We generated a BC1F2:3 population by propagating single plants from the BC1F2 populations for an additional generation. The phenotypic evaluation was conducted using mixed single plants from each family in the BC1F2:3 population, with the average phenotype serving as the phenotype for each individual. Subsequent QTL analysis revealed the presence of twenty-four QTLs, including eleven for GL, five for GW, six for LWR, and two for GT (Figure 2, Table 1).
Based on the above QTL mapping, qGW1, qGW5, qGW7, qGL9, and qGW12 were identified as significant contributors to grain size (Table 1). Five BC1F6 and BC1F7 populations developed from the BC1F2:3 line that were heterozygous in the target QTL region and homozygous for most of the other grain size QTLs were chosen to precisely evaluate their effects on grain size, respectively.

2.3. The Genetic Effect of qGW1

The grain width showed significant differences between NIL with homologous W240 (NIL-W) and NIL with homologous GZ63-4S (NIL-G) for qGW1 (Figure 3A,B and Figure S2). To fine map qGW1, we developed a BC1F8 population consisting of 2994 plants and subsequently identified 478 recombinants using markers RM128 and RM319. Eventually, we localized qGW1 to a high-resolution linkage map by progeny testing 36 recombinants and narrowed the qGW1 locus to a 231 kb region between markers R1344 and R1346 (Figure 3C).
Grain size is restricted by the spikelet hull, which is determined by cell division and expansion [45]. We conducted scanning electron microscopic (SEM) analysis to uncover the cytological basis underlying the regulation of grain size in qGW1. The values of cell width and the number of transverse cells were higher in NIL-WqGW1 than in NIL-GqGW1 (Figure 3D–I). Furthermore, we examined the expression of cell cycle and cell expansion genes in young panicles of NILs using qRT-PCR. Our findings indicated that four cell cycle related-genes (E2F2, CDKA1, CYCA3;1, and CYCT1), as well as four cell expansion related-genes (EXPA10, EXPA5, EXPA6, and EXPA7), were up-regulated in NIL-WqGW1, suggesting that qGW1 regulates grain width by promoting both cell division and cell expansion (Figure 3J and Figure 9).

2.4. The Genetic Effect of qGW5

Compared with NIL-WqGW5, the values of grain length were significantly higher in NIL-GqGW5 (Figure 4A,B and Figure S3). A progeny test of homozygous segregates further narrowed down the qGW5 locus to a 128 kb region between R5101 and R51142 (Figure 4C).
NIL-GqGW5 displayed significantly larger cell length and a higher number of longitudinal cells than that of NIL-WqGW5 (Figure 4D–I). Expression analysis revealed that five genes related to the cell cycle (E2F2, CDKA2, CYCA3;1, MAPK, and MCM3) and four genes related to the cell expansion (EXPA10, EXPA3, EXPA4, and EXPB7) showed higher expression levels in NIL-GqGW5. These findings revealed that qGW5 regulates grain length by influencing both cell division and expansion in the spikelet hull (Figure 4J and Figure 9).

2.5. The Genetic Effect of qGW7

The significant variations in grain width were observed among lines carrying different genotypes of qGW7 (Figure 5A,B and Figure S4). The qGW7 locus was ultimately mapped to a region of 444 kb between R7277 and R7281 using 1680 plants from the BC1F9 population (Figure 5C).
The NIL-WqGW7 spikelet hull had a wider cell size than NIL-GqGW7, while no difference was observed in cell number (Figure 5D–I). We further observed an up-regulation in the expression of six cell expansion related-genes (EXPA10, EXPA3, EXPA5, EXPA6, EXPA7 and EXPB7) in NIL-WqGW7, indicating that qGW7 regulates grain width through the alteration of cell expansion (Figure 5J and Figure 9).

2.6. The Genetic Effect of qGL9

NIL-GqGL9 had significantly larger grain length than NIL-WqGL9 (Figure 6A,B and Figure S5). Subsequently, we mapped the qGL9 to a 335 kb region between markers R9194 and R9197 in the BC1F9 generation, which included a total of 1584 plants (Figure 6C).
The number of longitudinal cells in the spikelet hull was higher in NIL-GqGL9 than in NIL-WqGL9. However, there was no difference in cell length between these two NILs (Figure 6D–I). Notably, NIL-GqGL9 exhibited up-regulated expression levels of fifteen genes related to cell cycle (CDC20, CDKA2, CDKB, CYCA2.1, CYCA3;2, CYCB1;1, CYCB2.2, CYCD1;1, CYCD4, CYCD6, H1, KN, MAPK, MCM2, and MCM4), resulting in increased cell division within the spikelet and ultimately leading to an increase in grain length (Figure 6J and Figure 9).

2.7. The Genetic Effect of qGW12

We constructed NIL-GqGW12 and confirmed that this allele could significantly increase grain width (Figure 7A,B and Figure S6). Subsequently, qGW12 was mapped within a 624 kb interval flanked by markers R12246 and R12252 (Figure 7C).
Analysis of the outer glume found that NIL-GqGW12 exhibited increased cell number and larger cell size in the grain-width direction, causing wider grain (Figure 7D–I). In addition, the expression levels of six genes related to cell cycle (CDKA1, CYCB1;1, CYCD1;1, CYCD3, CYCIaZm, and KN) and three genes related to cell expansion (EXPA6, EXPB4, and EXPB7) were higher in NIL-GqGW12 than in NIL-WqGW12. Thus, the up-regulation of both cell division and expansion genes was responsible for the increase in the grain width of NIL-GqGW12 (Figure 7J and Figure 9).

2.8. Investigation of Traits Related to Rice Yield and Quality in Five NILs

Finally, we performed phenotypic comparisons of yield- and quality-related traits among five NILs. The grain length of NIL-GqGW1, NIL-WqGW5, and NIL-WqGL9 exhibited significant reductions than their respective NILs, while no differences were observed in other NILs. Phenotypic variations were detected among the five NILs for grain width and length to width ration. Specifically, larger variations in 1000-grain weight and the number of tillers per plant were found in NIL-WqGW1 and NIL-WqGL9, whereas higher plant height and the number of filled grains per panicle were observed in NIL-GqGW7 and NIL-WqGW12. Grain yield per plant was significantly higher in NIL-GqGW1, NIL-GqGW7, NIL-GqGL9, and NIL-WqGW12 than that of their respective NILs. However, there were no differences in grain yield per plant between NIL-WqGW5 and NIL-GqGW5 (Figure 8A–H). These results suggested that the increased yield of qGW1 and qGL9 were primarily attributed to enhanced number of tillers per plant, while the increased yield of qGW7 and qGW12 were mainly due to enhanced numbers of filled grains per panicle.
There were no differences in albumin content among the five NILs. In contrast, three, four and five NILs exhibited remarkable variations in globulin, prolamin, and glutenin content, respectively. Compared with NIL-WqGW5, NIL-GqGW5 displayed higher total starch and amylose content, whereas no differences were observed in the other four NILs. NIL-WqGW5 and NIL-GqGL9 exhibited significant increases in gel consistency, while NIL-GqGW1, NIL-GqGW5, NIL-WqGW7, NIL-GqGL9, and NIL-GqGW12 showed huge improvements in taste score (Figure 8I–P). These findings indicated that the improved cooking and eating quality of qGW1 and qGL9 results from reduced globulin and glutenin content, whereas the improved quality of qGW7 and qGW12 derives from decreased prolamin and glutenin content. In addition, both decreased globulin and glutenin content and increased total starch content, amylose content, and gel consistency lead to an increase in the cooking and eating quality of qGW5. Usually, rice with lower protein content but higher amylose content and gel consistency exhibits superior cooking and eating quality [46].
In summary, negative correlations were observed between yield and rice quality for both qGW7 and qGW12. The impact of qGW5 on yield was negligible. Conversely, it exerted significant influences on quality-related traits, suggesting that this locus could enhance rice quality without compromising yield. Meanwhile, we provide two promising QTLs, qGW1 and qGL9, for breeding rice with a high yield and superior quality.

3. Discussion

3.1. QTL Mapping for Grain Size and Weight

In previous studies of QTL localization, mapping populations were typically constructed using parents with significant phenotypic and genetic disparities, resulting in the gradual cloning of dominant QTLs, such as GS3 [9,10], GS2 [30,31], GW2 [15], and GW5 [35,36].
As a primary isolated mapping population, the BC1F2 population offers the advantages of being relatively simple to construct, requiring less time, and providing rich genetic information. We generated a BC1F2:3 population by propagating single plants from the BC1F2 populations for an additional generation to address issues of offspring segregation and reproductive limitations caused by the BC1F2 population. Until now, many QTLs or genes have been reported from F2 and F2:3 populations, such as Rpsan 1, qPH9, FM1, Co-1HY, YrZ15-1949, qHBV4.2, qHBV6.1, qHBV11.1, and qHBV11.2 [47,48,49,50,51,52]. To identify novel QTLs controlling grain size, we developed BC1F2 and BC1F2:3 populations derived from W240 and GZ63-4S. A total of twenty-four QTLs for grain size were detected in this study (Figure 2, Table 1). By comparing chromosome positions and molecular markers, it was found that OsCKX1 exerts a negative regulatory effect on grain size within the same region as qGL1 [53]. GW2, which negatively controls grain width, was identified at the qGL2.2 locus [15]. Zhao et al. [54] detected two QTLs (qGL2.1 and qGW4) in regions similar to those of qGL2.1 and qGW4. Additionally, two other QTLs for grain length (qGL8.1 and qGL5) were, respectively, located in the vicinity of QTLs detected in previous studies [55,56].
Notably, we discovered five novel QTLs, qGW1, qGW5, qGW7, qGL9, and qGW12, which made significant contributions to grain size. Taken together, these results proved the reliability of using BC1F2 and BC1F2:3 populations for rice QTL mapping.

3.2. Validation and Fine Mapping of the Five QTLs

In previous studies, many genes controlling grain size have been cloned using the map-based cloning approach, such as GW2 [15], GS2 [30,31], and GL3.1 [37,38,39]. Therefore, QTL mapping, validation, and fine mapping are essential steps in the process of gene cloning.
In this study, we successfully validated five QTLs (qGW1, qGW5, qGW7, qGL9, and qGW12) that exert significant influences on grain size and narrow down their locations to regions ranging from 128 kb to 624 kb using a map-based cloning method. These results laid the foundation for the additional fine mapping of the five QTLs, the cloning of the candidate genes, and functional research to explore the genetic mechanisms underlying grain size.

3.3. Candidate Gene Analysis of the Five QTLs

According to the Nipponbare genome, LOC_Os01g59660 (MYBGA), LOC_Os01g59760 (OsbZIP09), and LOC_Os01g59780 are potential candidate genes for qGW1. Among these genes, LOC_Os01g59660, which encodes an MYB family transcription factor, has been shown to influence the development of floral organs, tiller number, and grain yield [57,58]. As we all know, MYB proteins play critical roles in development, metabolism, biotic stresses, and abiotic stresses [59,60]. Other members of the MYB family transcription factor, LOC_Os01g49160 (RGN1) and LOC_Os08g06110 (Nhd1), have been reported to affect rice gain size [61,62], while LOC_Os01g7402 (OsLUX) has been displayed to influence rice yield and heading date [63]. What is more, mutants of LOC_Os01g59760 (OsbZIP09), encoding a bZIP transcription factor, exhibited longer seeds compared with the wild type [64]. It has been reported that LOC_Os09g34880 (OsbZIP76), LOC_Os06g50600 (OsbZIP55), and LOC_Os06g15480 (OsbZIP47) function as regulators of grain size [65,66,67]. Furthermore, LOC_Os01g59780 encodes a protein that contains an AP2 domain. The homologous family genes, including LOC_Os07g47330 (FZP), LOC_Os05g03040 (RSR1), LOC_Os05g27930 (OsDREB2B), and LOC_Os07g13170 (OsSNB), have been studied for their role in controlling seed size in rice [68,69,70,71,72].
In the qGW5 interval, LOC_Os05g03020, a C2H2 zinc finger protein, could potentially be considered as a candidate gene. LOC_Os04g36650 (NSG1) and LOC_Os06g48530 (Du13), belonging to the C2H2 zinc finger family, have been found to determine grain size [73,74].
LOC_Os07g46460 (spl32) and LOC_Os07g46590 may be the candidate genes for qGW7. Compared with the wide type, the spl32 mutant exhibited a decrease in grain size [75]. Moreover, LOC_Os03g51230 (OsDDM1b), a homologous member of the gene family that contains LOC_Os07g46590, has been reported to regulate grain size [76].
There are three putative genes in the qGL9 region, which include LOC_Os09g32944 (OsSPL18), LOC_Os09g32740 (OsLMP1), and LOC_Os09g32948 (OsMADS8). Compared with the wild type, the mutant of OsSPL18 exhibited narrower and thinner grains [77]. Other members of the SPL family, such as OsSPL13, OsSPL12, and OsSPL16, played crucial roles in determining grain size [27,29,78]. Furthermore, LOC_Os02g14730 (LG1), a homologous member of the gene family containing LOC_Os09g32740, has been reported to positively control grain size [79]. What is more, MADS proteins play crucial roles in flower and fruit development [80]. Previous studies have demonstrated that OsMADS1, OsMADS34, and OsMADS56 exert significant influence on grain size [14,81,82].
LOC_Os12g40190 (OsXLG4), LOC_Os12g40570 (OsWRKY83), LOC_Os12g40460, and LOC_Os12g40830 are potential candidate genes for qGW12. Firstly, RGA1, a homologous member of the gene family containing LOC_Os12g40190 (OsXLG4), positively regulates rice grain size [83]. Secondly, OsWRKY36, a homologous member of the gene family containing LOC_Os12g40570 (OsWRKY83), has been reported to suppress grain size by inhibiting GA signaling [84]. Thirdly, DGS1, a homologous member of the gene family containing LOC_Os12g40460, played a positive role in regulating grain size by binding to OsBZR1 [85]. The mutant of FRRP1 resulted in an increase in rice grain length [86]. Finally, OsFRK3, a homologous member of the pfkB family containing LOC_Os12g40830, was identified as a positive regulator of grain width and thickness through its influence on sugar metabolism [87]. In the future, transgenic studies will be conducted on these five QTLs to further elucidate the molecular mechanisms underlying grain size.

3.4. Cytological Analysis of the Five QTLs

To elucidate the characterization and commonality of differentially expressed genes influencing cell number and size in qGW1, qGW5, qGW7, qGL9, and qGW12, we analyzed the association among these genes involved in cell division and expansion. Specifically, we identified one specific differential gene in qGW1, two in qGW5, ten in qGL9, and three in qGW12. The regulatory modules of qGW1-qGW5, qGW1-qGW12, qGW5-qGL9, and qGL9-qGW12 exhibited differential expression in cell cycle-related genes, including E2F2 and CYCA3;1; CDKA1; MAPK and CDKA2; and CYCD1;1, CYCB1;1, and KN, respectively (Figure 9A,B). These findings suggest that the regulation of grain width by qGW1 and qGW12 is mediated through the modulation of CDKA1, while the influence of qGW5 and qGL9 on grain length involves MAPK- and CDKA2-mediated mechanisms, thereby altering cell numbers. Moreover, the differential expression of cell expansion-related genes, such as EXPA5, EXPA10, EXPA6 and EXPA7; EXPA3, EXPA10, and EXPB7; EXPA10; and EXPA6 and EXPB7, were observed within the regulatory modules of qGW1-qGW7, qGW5-qGW7, qGW1-qGW5-qGW7, qGW1-qGW7-qGW12, and qGW5-qGW7-qGW12, respectively (Figure 9A,B), suggesting qGW1-qGW7-qGW12 influences cell size and grain width by altering the expression of EXPA6. Numerous QTLs and genes have been reported to regulate grain size by influencing the expression of cell division and expansion-related genes, such as qTGW2b [88], GLW7.1 [89], GL3.1 [38], and GS2 [90]. In addition, negative correlations were observed between gene expression and grain yield for both qGW1 and qGW7. Despite variations in the expression of cell cycle- and expansion-related genes, there was no alteration in the yield of qGW5. Notably, positive correlations were observed between gene expression and grain yield for both qGL9 and qGW12. Variations in the expression of genes associated with cell division and expansion not only impact grain size, but also influence grain yield [38,89,90].
Understanding the relationship between different genes involved in cell division and expansion is convenient for studying the shared characteristics and cytological molecular mechanisms underlying the influence of these five QTLs on rice grain size.

3.5. Potential Uses of the Five QTLs in Rice Breeding

High yield and superior quality are essential goals of rice breeders. Gene cloning and molecular breeding have become important techniques to breed high-yield and superior-quality varieties. The major genes that increase grain size, such as GW2 [15], GS2 [31], and GW5 [35,36], produce higher grain yield while simultaneously reducing grain quality. The gs9 allele has the potential to improve the appearance quality of milled rice without affecting grain yield [32]. Nevertheless, research advances have revealed several genes that could be utilized to help breeders develop new elite rice varieties with high yield and superior quality. The combination of the OsMADS1lgy3 allele with dep1-1 and gs3 alleles has the potential to simultaneously improve both grain yield and quality in rice [14]. GLW7.1 also represents a novel way to breed high-yield and superior-quality varieties [89].
In our study, negative correlations were observed between yield and rice quality for both qGW7 and qGW12. However, qGW5 exhibited the potential to enhance quality without compromising yield. Interestingly, qGW1 and qGL9 displayed positive correlations between grain yield and rice quality, indicating their pleiotropic effects in simultaneously improving both yield and quality. In summary, the identification of genetic resources, such as qGW5, qGW1, and qGL9, provides a theoretical foundation for breeding strategies aimed at enhancing grain yield and quality in rice. Our results laid the foundation for cloning these five genes. Additionally, such information will help breeders to improve grain yield and quality in rice.

4. Materials and Methods

4.1. Population Development and Field Experiment

The BC1F2 and BC1F2:3 populations were derived from two indica lines, GZ63-4S (the recurrent parent) and W240 (the donor parent). GZ63-4S is a photoperiod-thermo-sensitive genic male sterile indica rice line, carrying the infertility gene of TMS5 [91]. W240 is an indica variety with larger grain size. The BC1F2 and its derived BC1F2:3 populations were used for QTL mapping. To validate the genetic effects of qGW1, qGW5, qGW7, qGL9, and qGW12, five BC1F6 and BC1F7 populations were planted at the experimental station of Huazhong Agricultural University at Wuhan, Hubei province and Lingshui, Hainan province in 2020, respectively. The progeny tests were conducted in the BC1F8 and BC1F9 generations. The BC1F2, BC1F2:3, BC1F6, BC1F7, BC1F8, and BC1F9 populations were planted in 2016, 2017, 2020 (twice), and 2021 (twice). The detailed process of population development is shown in Figure S2. All rice plants with a density of 16 cm × 26 cm were grown under normal field management. Field management followed local practices. Ten plants were harvested from the middle of each row for trait measurement.

4.2. Trait Measurement

Harvested rice grains from each plant were air-dried and stored at room temperature for three months before testing. Grain length, grain width, grain number, grain yield, and 1000-grain weight were measured using the yield traits scorer (YTS) platform [92], whereas grain thickness was measured using vernier calipers. The plant height was measured from the main culm. The number of tillers per plant was counted as all fertile panicles in one plant. Additionally, flour ground from milled grain was used to determine the albumin content, globulin content, prolamin content, glutenin content, total starch content, amylose content, and gel consistency according to the NY/T 593-2013 standard published by the Ministry of Agriculture, China (http://www.zbgb.org/27/StandardDetail1476335.htm, accessed on 3 October 2019). Taste scores for milled rice were evaluated using a taste analyzer kit (Satake, RLTA10B-KC, Hiroshima, Japan) [93].

4.3. Genetic Map Construction and QTL Mapping

The parent varieties GZ63-4S and W240 were sequenced using the illumine HiSeq2000 (Illumina, San Diego, CA, USA), and the sequencing data were compared and assembled according to the rice reference genome (Rice Genome Annotation Project, http://rice.uga.edu/, accessed on 6 February 2022) [94]. All mapping primers were designed in reference to the sequencing data of two parents. A total of 67 polymorphic simple sequence repeat (SSR) markers, 103 insert and deletion (InDel) markers, and 7 Kompetitive allele-specific PCR (KASP) markers were evenly distributed across 12 chromosomes to genotype the 327 BC1F2 lines. According to the cetyltrimethylammonium bromide (CTAB) method, genomic DNA was extracted from leaves [95]. The genotyping was carried out using 4% Polyacrylamide gels (PAGE) migration, as previously reported by Panaud et al. [96]. DNA bands on PAGE gel were displayed by silver nitrate staining and NaOH-formaldehyde solution. Combining the genotype data from the BC1F6 lines and the phenotype data from both BC1F6 and BC1F7 lines, we employed the Kosambi mapping function of MapMaker/Exp3.0 program to construct a genetic linkage map [97]. QTL analysis was performed using the composite interval mapping method with Windows QTL cartographer 2.5 software (WinQTLCart 2.5) [98].
Refraining from considering the QTL of cloned genes, we selected five major QTLs that have higher LOD, Add, and PVE to study. The genotypes of the five BC1F6 and BC1F7 lines of qGW1, qGW5, qGW7, qGL9, and qGW12 were determined using two flanking markers within the mapping interval of QTLs. To fine map these QTLs, we developed five BC1F9 populations consisting of 1728, 2046, 1680, 1584, and 1440 individuals, respectively. Another 6, 5, 14, 5, and 6 specific markers were developed to genotype the recombinants of these QTLs. Relevant primer sequences are shown in Table S2.

4.4. Scanning Election Microscopy

Lemmas of spikelets at the heading stage were collected for scanning electron microscopy, fixed in FAA solution (50% ethanol, 5% glacial acetic acid, and 3.7% formaldehyde) at 4 °C for 24 h. The young panicles were sampled at the length of about 3 cm. Then the samples were coated with gold under vacuum conditions, and observed using a scanning electron microscope (JEOL, JSM-6390LV, Tokyo, Japan) under 10 kV acceleration voltage and a 30 nm spot size. Cell number and cell size were calculated at 50 × and 100 × magnification, respectively. The spikelet epidermal cell size was measured using Image J software (NIH), and cell number was counted manually. Scanning electron microscopy analysis involved at least three biological replications of mounted specimens.

4.5. RNA Extraction, Reverse Transcription, and qRT-PCR

Total RNA was extracted from young panicles using the TRIzol method (Invitrogen, 15596026, Shanghai, China), and then treated with RNase-free DNase I (Invitrogen, 15596026, Shanghai, China). First, strand cDNA was reverse-transcribed using the M-MLV Reverse Transcriptase kit (Promega, M170A, Madison, WI, USA). All procedures were carried out according to the manufacturer’s protocol. qRT-PCR was performed using ABI Real-Time PCR system with the SYBR Green I mix (TaKaRa, Shiga, Japan) according to the manufacturer’s instructions. OsActin gene was used as an internal control to normalize gene expression. The gene expression levels in three biological replicates and three technical replicates were calculated to evaluate the significance of differences between samples using the student’s t-test. Relevant primer sequences are given in Table S2.

4.6. Statistical Analysis

Differences between two sets of data were presented as the mean ± standard deviation and performed using the student’s t-test. We conducted differential expression analysis of differentially expressed genes using Cytoscape software (3.9.1) [99] and the Metware Cloud, a free online platform for data analysis (https://cloud.metware.cn, accessed on 1 January 2024).

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms25084149/s1, Figure S1: Correlation analysis of GL, GW, LWR, and GT in 2016 (A) and 2017 (B). Correlation analysis of grain length (C), grain width (D), length to width (E), and grain thickness (F) in two years; Figure S2: Development of the rice populations used in this study; Figure S3: Frequency distributions of qGW1 in the BC1F7 and BC1F8 populations; Figure S4: Frequency distributions of qGW5 in the BC1F7 and BC1F8 populations; Figure S5: Frequency distributions of qGW7 in the BC1F7 and BC1F8 populations; Figure S6: Frequency distributions of qGL9 in the BC1F7 and BC1F8 populations; Figure S7: Frequency distributions of qGW12 in the BC1F7 and BC1F8 populations; Table S1: The phenotypic performance of four grain size traits in W240 and GZ63-4S; Table S2: Primers used in this study.

Author Contributions

Y.Z. performed most of the experiments, including phenotypic correlation analysis, QTL mapping, verification, fine mapping, scanning electron microscopic analysis, expression analysis, and investigation of yield- and quality-related phenotypes. H.Y., E.L., M.A. and H.G. assisted with the experiments. R.L. participated in revising the manuscript. G.G., Q.Z. and L.X. participated in field management and logistics. Y.L. helped with phenotype. Y.H. designed and supervised the study. Y.Z. and Y.H. wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by grants from the Ministry of Science and Technology (2022YFD1200100, 2021YFF1000200), National Natural Science Foundation of China (U21A20211, 31821005), AgroST Project (NK20220501) and China Agriculture Research System (CARS-01-03).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

For materials, please contact the corresponding author’s email address.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Frequency distributions of GL (A), GW (B), LWR (C), and GT (D) in the BC1F2 and BC1F2:3 populations. The vertical axis represents the number of BC1F2 and BC1F2:3 plants, with black and gray bars, respectively.
Figure 1. Frequency distributions of GL (A), GW (B), LWR (C), and GT (D) in the BC1F2 and BC1F2:3 populations. The vertical axis represents the number of BC1F2 and BC1F2:3 plants, with black and gray bars, respectively.
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Figure 2. Genetic linkage map of grain-size-related QTLs detected in the BC1F2 and BC1F2:3 populations.
Figure 2. Genetic linkage map of grain-size-related QTLs detected in the BC1F2 and BC1F2:3 populations.
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Figure 3. Analysis of qGW1 influence grain width. (A) Grain morphology. Scale bar: 5 mm. (B) Grain width difference among three haplotypes in 2020. (C) Fine mapping of qGW1. The numbers below the bar are physical distance (Mb). (D,E) Scanning electron microscopy of the outer epidermal cells of NIL-WqGW1 and NIL-GqGW1. Scale bar: 100 µm. (F) Cell length. (G) Total number of longitudinal cells. (H) Cell width. (I) Total number of transverse cells. (n = 10). (J) qRT-PCR analysis of four cell cycle related-genes and four cell expansion related-genes between NILs of qGW1. Data are represented as mean ± s.e.m. (n = 9). Duncan’s multiple range tests were used to conduct statistical analysis (a, b and c indicate p < 0.01). The student’s t-test was used to produce p values (*, ** indicate significance at p < 0.05 and p < 0.01, respectively).
Figure 3. Analysis of qGW1 influence grain width. (A) Grain morphology. Scale bar: 5 mm. (B) Grain width difference among three haplotypes in 2020. (C) Fine mapping of qGW1. The numbers below the bar are physical distance (Mb). (D,E) Scanning electron microscopy of the outer epidermal cells of NIL-WqGW1 and NIL-GqGW1. Scale bar: 100 µm. (F) Cell length. (G) Total number of longitudinal cells. (H) Cell width. (I) Total number of transverse cells. (n = 10). (J) qRT-PCR analysis of four cell cycle related-genes and four cell expansion related-genes between NILs of qGW1. Data are represented as mean ± s.e.m. (n = 9). Duncan’s multiple range tests were used to conduct statistical analysis (a, b and c indicate p < 0.01). The student’s t-test was used to produce p values (*, ** indicate significance at p < 0.05 and p < 0.01, respectively).
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Figure 4. Analysis of qGW5 influence grain length. (A) Grain morphology. Scale bar: 5 mm. (B) Grain length difference among three haplotypes in 2020. (C) Fine mapping of qGW5. The numbers below the bar are physical distance (Mb). (D,E) Scanning electron microscopy of the outer epidermal cells of NIL-WqGW5 and NIL-GqGW5. Scale bar: 100 µm. (F) Cell length. (G) Total number of longitudinal cells. (H) Cell width. (I) Total number of transverse cells. (n = 10). (J) qRT-PCR analysis of five cell cycle related-genes and four cell expansion related-genes between NILs of qGW5. Data are represented as mean ± s.e.m. (n = 9). Duncan’s multiple range tests were used to conduct statistical analysis (a, b and c indicate p < 0.01). The student’s t-test was used to produce p values (*, ** indicate significance at p < 0.05 and p < 0.01, respectively).
Figure 4. Analysis of qGW5 influence grain length. (A) Grain morphology. Scale bar: 5 mm. (B) Grain length difference among three haplotypes in 2020. (C) Fine mapping of qGW5. The numbers below the bar are physical distance (Mb). (D,E) Scanning electron microscopy of the outer epidermal cells of NIL-WqGW5 and NIL-GqGW5. Scale bar: 100 µm. (F) Cell length. (G) Total number of longitudinal cells. (H) Cell width. (I) Total number of transverse cells. (n = 10). (J) qRT-PCR analysis of five cell cycle related-genes and four cell expansion related-genes between NILs of qGW5. Data are represented as mean ± s.e.m. (n = 9). Duncan’s multiple range tests were used to conduct statistical analysis (a, b and c indicate p < 0.01). The student’s t-test was used to produce p values (*, ** indicate significance at p < 0.05 and p < 0.01, respectively).
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Figure 5. Analysis of qGW7 influence grain width. (A) Grain morphology. Scale bar: 5 mm. (B) Grain width difference among three haplotypes in 2020. (C) Fine mapping of qGW7. The numbers below the bar are physical distance (Mb). (D,E) Scanning electron microscopy of the outer epidermal cells of NIL-WqGW7 and NIL-GqGW7. Scale bar: 100 µm. (F) Cell length. (G) Total number of longitudinal cells. (H) Cell width. (I) Total number of transverse cells. (n = 10). (J) qRT-PCR analysis of six cell expansion related-genes between NILs of qGW7. Data are represented as mean ± s.e.m. (n = 9). Duncan’s multiple range tests were used to conduct statistical analysis (a, b and c indicate p < 0.01). The student’s t-test was used to produce p values (*, ** indicate significance at p < 0.05 and p < 0.01, respectively).
Figure 5. Analysis of qGW7 influence grain width. (A) Grain morphology. Scale bar: 5 mm. (B) Grain width difference among three haplotypes in 2020. (C) Fine mapping of qGW7. The numbers below the bar are physical distance (Mb). (D,E) Scanning electron microscopy of the outer epidermal cells of NIL-WqGW7 and NIL-GqGW7. Scale bar: 100 µm. (F) Cell length. (G) Total number of longitudinal cells. (H) Cell width. (I) Total number of transverse cells. (n = 10). (J) qRT-PCR analysis of six cell expansion related-genes between NILs of qGW7. Data are represented as mean ± s.e.m. (n = 9). Duncan’s multiple range tests were used to conduct statistical analysis (a, b and c indicate p < 0.01). The student’s t-test was used to produce p values (*, ** indicate significance at p < 0.05 and p < 0.01, respectively).
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Figure 6. Analysis of qGL9 influence grain length. (A) Grain morphology. Scale bar: 5 mm. (B) Grain length difference among three haplotypes in 2020. (C) Fine mapping of qGL9. The numbers below the bar are physical distance (Mb). (D,E) Scanning electron microscopy of the outer epidermal cells of NIL-WqGL9 and NIL-GqGL9. Scale bar: 100 µm. (F) Cell length. (G) Total number of longitudinal cells. (H) Cell width. (I) Total number of transverse cells. (n = 10). (J) qRT-PCR analysis of fifteen cell cycle related-genes between NILs of qGL9. Data are represented as mean ± s.e.m. (n = 9). Duncan’s multiple range tests were used to conduct statistical analysis (a, b and c indicate p < 0.01). The student’s t-test was used to produce p values (*, ** indicate significance at p < 0.05 and p < 0.01, respectively).
Figure 6. Analysis of qGL9 influence grain length. (A) Grain morphology. Scale bar: 5 mm. (B) Grain length difference among three haplotypes in 2020. (C) Fine mapping of qGL9. The numbers below the bar are physical distance (Mb). (D,E) Scanning electron microscopy of the outer epidermal cells of NIL-WqGL9 and NIL-GqGL9. Scale bar: 100 µm. (F) Cell length. (G) Total number of longitudinal cells. (H) Cell width. (I) Total number of transverse cells. (n = 10). (J) qRT-PCR analysis of fifteen cell cycle related-genes between NILs of qGL9. Data are represented as mean ± s.e.m. (n = 9). Duncan’s multiple range tests were used to conduct statistical analysis (a, b and c indicate p < 0.01). The student’s t-test was used to produce p values (*, ** indicate significance at p < 0.05 and p < 0.01, respectively).
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Figure 7. Analysis of qGW12 influence grain width. (A) Grain morphology. Scale bar: 5 mm. (B) Grain width difference among three haplotypes in 2020. (C) Fine mapping of qGW12. The numbers below the bar are physical distance (Mb). (D,E) Scanning electron microscopy of the outer epidermal cells of NIL-WqGW12 and NIL-GqGW12. Scale bar: 100 µm. (F) Cell length. (G) Total number of longitudinal cells. (H) Cell width. (I) Total number of transverse cells. (n = 10). (J) qRT-PCR analysis of six cell cycle related-genes and three cell expansion related-genes between NILs of qGW12. Data are represented as mean ± s.e.m. (n = 9). Duncan’s multiple range tests were used to conduct statistical analysis (a and b indicate p < 0.01). The student’s t-test was used to produce p values (*, ** indicate significance at p < 0.05 and p < 0.01, respectively).
Figure 7. Analysis of qGW12 influence grain width. (A) Grain morphology. Scale bar: 5 mm. (B) Grain width difference among three haplotypes in 2020. (C) Fine mapping of qGW12. The numbers below the bar are physical distance (Mb). (D,E) Scanning electron microscopy of the outer epidermal cells of NIL-WqGW12 and NIL-GqGW12. Scale bar: 100 µm. (F) Cell length. (G) Total number of longitudinal cells. (H) Cell width. (I) Total number of transverse cells. (n = 10). (J) qRT-PCR analysis of six cell cycle related-genes and three cell expansion related-genes between NILs of qGW12. Data are represented as mean ± s.e.m. (n = 9). Duncan’s multiple range tests were used to conduct statistical analysis (a and b indicate p < 0.01). The student’s t-test was used to produce p values (*, ** indicate significance at p < 0.05 and p < 0.01, respectively).
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Figure 8. Phenotypes of rice yield and quality comparison among qGW1, qGW5, qGW7, qGL9, and qGW12 identified in this study. (A–H) Grain length, grain width, length to width ratio, 1000-grain weight, plant height, number of tillers per plant, number of filled grains per panicle, and grain yield per plant in five NILs. (n = 12). (I–P) Albumin content, globulin content, prolamin content, glutenin content, total starch content, amylose content, gel consistency, and taste score. (n = 6). All phenotypic data in (A–P) were measured from paddy-grown NIL plants grown under normal cultivation conditions. Data are represented as mean ± s.e.m. The student’s t-test was used to produce p values (*, ** indicate significance at p < 0.05 and p < 0.01, respectively).
Figure 8. Phenotypes of rice yield and quality comparison among qGW1, qGW5, qGW7, qGL9, and qGW12 identified in this study. (A–H) Grain length, grain width, length to width ratio, 1000-grain weight, plant height, number of tillers per plant, number of filled grains per panicle, and grain yield per plant in five NILs. (n = 12). (I–P) Albumin content, globulin content, prolamin content, glutenin content, total starch content, amylose content, gel consistency, and taste score. (n = 6). All phenotypic data in (A–P) were measured from paddy-grown NIL plants grown under normal cultivation conditions. Data are represented as mean ± s.e.m. The student’s t-test was used to produce p values (*, ** indicate significance at p < 0.05 and p < 0.01, respectively).
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Figure 9. The differential expression analysis of (A,B) cell number and size genes.
Figure 9. The differential expression analysis of (A,B) cell number and size genes.
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Table 1. QTLs for grain size in the BC1F2 and BC1F2:3 populations.
Table 1. QTLs for grain size in the BC1F2 and BC1F2:3 populations.
QTLChromosome Interval20162017Known Genes
LODAddPVE (%)LODAddPVE (%)
qGL11RM220RM49015.78 0.20 14.82 16.42 0.21 21.56 OsCKX1
qGL2.12RM211R24794.95 −0.05 0.42 7.98 −0.09 3.09
qGL2.22R2746R2100 2.60 −0.21 14.04 GW2
qGL2.32R2100R2178 2.54 −0.09 5.84
qGL2.42R2198RM262 3.62 0.07 8.92
qGL55RM169RM1634.77 −0.07 1.50 3.28 −0.06 1.47
qGL8.18R8354R84642.73 −0.05 0.24 4.00 −0.06 1.81
qGL8.28R8173R82742.85 −0.03 0.06
qGL99R9178RM1605.66 −0.1311.87 9.07 −0.1512.26
qGL1111RM21R11264.74 −0.09 2.15
qGL1212R1224R12264.46 −0.066.04
qGW11RM128RM319 2.63 0.20 13.24
qGW44R4306R43144.82 −0.04 5.71
qGW55RM122R51472.680.10 23.325.24 0.04 18.33
qGW77RM234RM2484.150.046.272.68 0.0715.01
qGW1212R1224R1226 6.29−0.058.95
qLWR1.11RM220RM4903.67 0.05 3.31 3.37 0.04 2.72 OsCKX1
qLWR1.21RM128RM319 2.81 −0.03 1.71
qLWR55RM122R51473.23 −0.05 4.37 2.52 −0.04 3.10
qLWR88R8173R82742.78 −0.02 0.16
qLWR99R9178RM160 5.93 −0.04 2.99
qLWR1212R1224R1226 4.02 0.04 8.81
qGT11RM319R1386 3.58−0.017.67
qGT22R2470RM2117.20 −0.0321.23
qGL, QTL for grain length; qGW, QTL for grain width; qLWR, QTL for length to width ration; qGT, QTL for grain thickness. LOD, logarithm of odds. Add, additive effect of QTL. Positive value and negative value of additive effects indicated the W240 and GZ63-4S alleles, respectively. PVE, phenotypic variance explained by the QTL.
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Zhou, Y.; Yang, H.; Liu, E.; Liu, R.; Alam, M.; Gao, H.; Gao, G.; Zhang, Q.; Li, Y.; Xiong, L.; et al. Fine Mapping of Five Grain Size QTLs Which Affect Grain Yield and Quality in Rice. Int. J. Mol. Sci. 2024, 25, 4149. https://doi.org/10.3390/ijms25084149

AMA Style

Zhou Y, Yang H, Liu E, Liu R, Alam M, Gao H, Gao G, Zhang Q, Li Y, Xiong L, et al. Fine Mapping of Five Grain Size QTLs Which Affect Grain Yield and Quality in Rice. International Journal of Molecular Sciences. 2024; 25(8):4149. https://doi.org/10.3390/ijms25084149

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Zhou, Yin, Hanyuan Yang, Enyu Liu, Rongjia Liu, Mufid Alam, Haozhou Gao, Guanjun Gao, Qinglu Zhang, Yanhua Li, Lizhong Xiong, and et al. 2024. "Fine Mapping of Five Grain Size QTLs Which Affect Grain Yield and Quality in Rice" International Journal of Molecular Sciences 25, no. 8: 4149. https://doi.org/10.3390/ijms25084149

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