Identification and characterization of stable QTLs for vascular bundle number at the panicle neck in rice (Oryza sativa L.)

doi:10.21203/rs.3.rs-2466637/v1

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Identification and characterization of stable QTLs for vascular bundle number at the panicle neck in rice (Oryza sativa L.)

https://doi.org/10.21203/rs.3.rs-2466637/v1

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Vascular bundles are responsible for transporting photosynthesis products, minerals, and water from source to sink. A large vascular bundle number (VBN) in the panicle neck is related to the ability to transport assimilates from stem and leaf to reproductive organs during seed maturation. Several quantitative trait loci (QTLs) for VBN with major effects have been identified by using segregating populations derived from a cross between indica and japonica rice cultivars. However, there are few reports of QTLs for VBN with minor effects. Here, we identified three stable QTLs for VBN—qVBN5 on chromosome 5, qVBN6 on chromosome 6, and qVBN11 on chromosome 11—by using 71 recombinant inbred lines derived from a cross between indica IR24 and japonica Asominori. We confirmed their positions and characterized their effects by using chromosome segment substitution lines (CSSLs) with an IR24 genetic background. qVBN6 had the most substantial effect on VBN, followed by qVBN11 and qVBN5. We developed pyramided lines carrying two QTLs for VBN to estimate their interaction. Two lines carrying both qVBN6 and qVBN11 had a considerably lower VBN than the parental lines, each carrying a single QTL. The combination accumulated VBN negatively in the pyramided lines owing to the independent actions of each QTL. The QTLs detected for VBN will enhance our understanding of genetic mechanisms of VBN and can be used in rice breeding.

rice

vascular bundle

stable QTLs

CSSLs

RILs

pyramided lines

Rice (Oryza sativa L.), an essential staple crop for more than half of the world’s population, is grown mainly in East, Southeast, and South Asia. Owing to the continuously increasing population size and loss of arable land resulting from land degradation and salinization of irrigated areas, increasing rice production per unit area is a major goal of breeders (Khush 1997). Grain yield is determined by four yield components: spikelet number per panicle, panicle number per plant, grain weight, and grain fertility. To increase yield potential, increasing the source size, sink size, and translocation capability are crucial ( Donald 1968; Lee and Chae 2000). The vascular system plays an important role in transporting photosynthesis products from source to sink, and its transport capacity influences grain yield (Fukuyama et al. 1999; Cui et al. 2003). In the development of hybrid rice, a small vascular bundle number (VBN) in the panicle neck restricts grain filling by limiting the transport of assimilates to the sink (Peng et al. 1999; Xu et al. 2005).

Vascular bundles interconnect all parts of a plant and are responsible for transporting photosynthesis products, minerals, and water around (Lucas et al. 2013). Rice has two types of vascular bundles: small ones in the rudimentary glumes of spikelets, and large ones that play a major role in nutrient uptake and ripening rate, which in turn affect grain weight (Chaudhry and Nagato 1970). Wide genetic diversity among rice cultivars underlies the number of large vascular bundles in the panicle neck; the VBN of indica rice tends to be greater than that of japonica rice (Fukuyama and Takayama 1995; Fukuyama et al. 1999; Liu et al. 2016; Zhai et al. 2018); and the ratio of VBN to primary branch number differs between them (Fukuyama and Takayama 1995; Fukuyama et al. 1999).

VBN is influenced mainly by inherited factors but also by environmental factors such as nitrogen availability and plant density. Through the use of segregating populations such as recombinant inbred lines (RILs) and double haploid lines (DHs), genetic factors that control VBN in the panicle neck of O. sativa have been detected as quantitative trait loci (QTLs) (Bai et al. 2012; Cui et al. 2003; Sasahara et al. 1999; Zhang et al. 2002); and genome-wide association studies (GWAS) have identified QTLs for VBN (Liao et al. 2021; Zhai et al. 2018). Most detected QTLs for VBN had major effects with a high percentage of phenotypic variation explained (PVE) under different environments (Paterson et al. 1991; Tanksley 1993). In contrast, QTLs with minor effects were influenced by environmental factors and were not detected by QTL analysis (Collard et al. 2005; Yamamoto et al. 2009). Some genes underlying major QTLs for VBN have been isolated to their chromosomal regions and cloned, and include ABERRANT PANICLE ORGANIZATION 1 (APO1) on chromosome (Chr.) 6 (Terao et al. 2010), NARROW LEAF 1 (NAL1) on Chr. 4 (Fujita et al. 2013; Qi et al. 2008), and LVB9/DENSE AND ERECT PANICLE 1 (DEP1) on Chr. 9 (Fei et al. 2019). However, QTLs with minor effects are poorly understood. Owing to the influence of environment and major QTLs, minor QTLs are not stably detected from year to year (Collard et al. 2005). To detect minor QTLs stably and precisely, it is necessary to use RILs and DHs for genetic analyses. By eliminating environmental effects in segregating populations when grown in different years, seasons, or environments, both can be more reliable for mapping genes (Collard et al. 2005; Kearsey and Farquhar 1998). Chromosome segment substitution lines (CSSLs) can be used for developing materials for confirming QTLs with minor effects and for evaluating QTL epistasis through pyramiding (Yamamoto et al. 2009).

In this study, RILs derived from a cross between indica rice IR24 and japonica rice Asominori were used to elucidate the genetic basis of VBN. Each detected QTL was confirmed in CSSLs carrying Asominori chromosomal segments in the IR24 genetic background. QTL analysis of VBN was conducted using RILs, and each QTL’s phenotypic effects were verified and validated using CSSLs. The effects of pyramiding pair of the QTLs were evaluated.

Plant materials

For QTL analysis in 2016, 2017, and 2018, we used 71 RILs derived from a cross between Asominori and IR24 followed by backcrosses by the single-seed-descent method (Tsunematsu et al. 1996). For evaluating the effect of QTLs in 2018 and 2019, we used three CSSLs with the IR24 genetic background—IAS30, IAS39, and IAS14—carrying target substitution chromosomal segments of Asominori (Kubo et al. 2002). For QTL confirmation in 2019, we used three F₂ populations derived from a cross between each of those CSSLs and IR24 (168 individuals each). To characterize the gene interactions among the detected QTLs, we developed and characterized pyramided lines (PYLs) each carrying two QTLs in the IR24 genetic background: we developed three F₁ plants from crosses between IAS30 (carrying qVBN5) and IAS39 (carrying qVBN6), IAS30 and IAS14 (carrying qVBN11), and IAS39 and IAS14. These F₁ plants were self-pollinated to develop F₂ populations of 96 plants each. PYLs were selected from each F₂ population by marker-assisted selection: PYLs 1–4 from IAS30/IAS14, PYLs 5–7 from IAS39/IAS14, and PYLs 8–10 from IAS30/IAS39. These 10 PYLs were self-pollinated, and the effects of pyramiding were evaluated in 24 plants of each F₃ line in 2020.

Plant growth condition and evaluation of VBN

Plants were grown in a paddy field of Saga University. At 28 days after sowing, seedlings were transplanted at one plant per hill with 20 cm between hills and 25 cm between rows. Inorganic fertilizer was applied at 40 kg/ha N, 17.5 kg/ha P, and 33 kg/ha K. At 2 to 3 weeks after heading, the tallest panicles were collected for counting of VBN in the panicle neck. Fresh peduncles were sliced at about 1 cm below the panicle base node, and the large vascular bundles were counted under a microscope, in 5 stems each of RILs and PYLs, 10 of CSSLs, and every plant in the F₂ populations.

Extracting DNA and genotyping

Approximately 2–4 cm of leaves was collected directly and freeze-dried for 48 h. Total DNA was extracted by the potassium acetate method (Dellaporta et al. 1983). Polymorphic SSR markers (McCouch et al. 2002) and indel markers (Yonemaru et al. 2015) were used for genotyping of segregating populations; these co-dominant markers distinguish between the two parental lines. Polymerase chain reaction (PCR) was performed using GoTaq master mix (Promega) at 96 ℃ for 5 min; 35 cycles of 30 s at 96 ℃, 30s at 55 ℃, and 30 s at 72 ℃; and a final at 25 ℃ for 1 min. The amplified PCR products were run in 4% agarose gel at 200 V with 0.5 µg/mL ethidium bromide in 0.5 × TBE buffer for 60–120 min.

DNA markers

QTLs in RILs were found using the genotyping data from RFLP markers each year (Tsunematsu et al. 1996). QTL analysis for VBN in each population used SSR markers located around the substituted chromosome locations in the CSSLs. We selected 15 SSR/indel markers on Chr. 5 around RFLP marker interval C128–R2117 (22.6–23.4 Mbp) for genotyping the IR24/IAS30 F₂ population; 7 markers on Chr. 6 around C962–Ky11 (~ 28.6 Mbp) for genotyping the IR24/IAS39 F₂; and 12 markers on Chr. 11 around R2918–C3029A (~ 2.2–2.4 Mbp) for genotyping the IR24/IAS14 F₂ (Supplemental Table S1).

QTL analysis

We performed composite interval mapping in Windows QTL Cartographer v. 2.5 software (Wang et al. 2012) to identify QTLs for VBN. The threshold logarithm of odds (LOD) score was determined as 3.0 by permutation tests performed 1000 times.

Identification of QTLs for VBN in RILs

The VBN of Asominori was 10.3–11.8 in all 3 years, and that of IR24 was 20.4–24.8 (Fig. 1). Those of the RILs were 10–23 in 2016, 11–23 in 2017, and 10–21 in 2018. The continuous frequency distributions of VBN in the RILs imply that multiple genetic factors control VBN.

Four QTLs for VBN were detected: qVBN5 and qVBN6 in 2016, 2017, and 2018; qVBN11 in 2016 and 2017; and qVBN4 in 2018 (Table 1). qVBN5 had PVE of 10.3–28.6%, qVBN6 of 13.6–18.3%, qVBN11 of 11.8–24.0%, and qVBN4 of 10.4%. The ‘Asominori’ alleles at qVBN5, qVBN6, and qVBN11 decreased VBN, whereas that at qVBN4 increased VBN.

Table 1

Detection of QTLs for vascular bundle number in RILs derived from IR24/Asominori.
Year	QTL	Chr.	Interval marker	LOD	Additive effect ^a	R² (%)
2016	qVBN5	5	C128–R2117	4.6	−1.27	16.3
	qVBN6	6	C962–Ky11	4.2	−1.31	16.3
	qVBN11	11	R2918-C3029A	3.4	−1.14	11.8
2017	qVBN5	5	C128-R2117	6.9	−1.67	28.6
	qVBN6	6	Xnpb135–C962	3.8	−1.21	13.6
	qVBN11	11	C794A–C83B	6.3	−2.19	24.0
2018	qVBN4	4	C335–C621B	3.5	0.84	10.4
	qVBN5	5	C128-R2117	3.4	−0.86	10.3
	qVBN6	11	C962-Ky11	5.2	−1.15	18.3
^a Additive effects indicate Asominori allele.

Validation of single QTL effects on VBN in IR24 genetic background

In 2018, VBN of IAS39 (carrying qVBN6) was 13.1, and that of IAS14 (carrying qVBN11) was 16.3 (Fig. 2), significantly lower than that of IR24 (21.2). On the other hand, VBN of IAS30 (carrying qVBN5) was 20.4, similar to that of IR24. The Asominori alleles at qVBN6 and qVBN11 decreased VBN by 8.1 in IAS39 and by 4.9 in IAS14. In 2019, VBN of IAS39 was 15.5 and that of IAS14 was 18.8, significantly lower than that of IR24 (22.1). On the other hand, VBN of IAS30 was 22.1, close to that of IR24. The Asominori alleles at qVBN6 and qVBN11 decreased VBN by 6.6 in IAS39 and by 3.3 in IAS14. The effects of the three QTLs were consistent between years, implying minor environmental influence on VBN. These results indicate that the Asominori alleles at qVBN6 and qVBN11 stably decreased VBN and validate their effects in the IR24 genetic background.

Confirmation of QTLs for VBN using CSSLs

VBN of the IR24/IAS30 F₂ population was 18–31, when that of IR24 was 23.2 and that of IAS30 was 22.6 (Fig. 3a). Two QTLs were detected on Chr. 5: qVBN5.1, with PVE of 12.6%, and qVBN5.2, with PVE of 13.7%. The Asominori alleles decreased VBN by 1.09 and 1.18, respectively (Table 2). VBN of the IR24/IAS14 F₂ population was 16–28, when that of IR24 was 22.2 and that of IAS14 was 17.6 (Fig. 3b). One QTL, qVBN11, was detected on Chr. 11, with PVE of 17.9%. The Asominori allele decreased VBN by 1.28 (Table 2).VBN of the IR24/IAS39 F₂ population was 14–29, when that of IR24 was 24.6 and that of IAS39 was 16.8 (Fig. 3c). One QTL, qVBN6, was detected on Chr. 6, with PVE of 61.9%. The Asominori allele at qVBN6 decreased VBN by 3.35 (Table 2).These results confirm the presence of QTLs for VBN, and that the Asominori alleles at qVBN6, qVBN5, and qVBN11 had negative effects on VBN.

Table 2

Detection of QTLs on Chrs. 5, 6, and 11 in CSSLs with IR24 background.
QTL	Chr.	Interval marker	Interval marker (Mbp) ^a	Additive effect ^b	LOD	Dominant effect	PVE (%)
qVBN5.1	5	RM18751–RM18821	21.11–22.52	−1.09	4.6	0.54	12.6
qVBN5.2	5	RM7081–RM7446	24.44–24.82	−1.18	5.0	0.75	13.7
qVBN6	6	RM20546–Indel493	27.02–27.65	−3.35	34.5	−1.45	61.9
qVBN11	11	Indel8807–Indel8810	1.18–1.28	−1.28	7.0	0.02	17.9
^a On Nipponbare genome sequence.
^b Negative sign indicates negative-effect Asominori allele.

Evaluation of pyramid effects of two QTLs for VBN in IR24 genetic background

From the IAS30/IAS14 F₂, PYLs 1–4 (qVBN5 + qVBN11) were selected by markers RM3351, RM6841, C5 Indel8795, and C5 Indel8837. From the IAS39/IAS14 F₂, PYLs 5–7 (qVBN6 + qVBN11) were selected by markers RM400, RM6395, C5 Indel8795, and C5 Indel8837. And from the IAS30/IAS39 F₂, PYLs 8–10 (qVBN5 + qVBN6) were selected by markers RM3351, RM6841, RM400, and RM6395. VBNs of PYLs 1–4 (qVBN5 + qVBN11) were 17.0–17.6, similar to that of IAS14 (qVBN11) but significantly lower than those of IAS30 (qVBN5) and IR24 (Fig. 4a). VBNs of PYLs 5–7 (qVBN6 + qVBN11) were 11.6–14.0. Those of PYLs 6 and 7 were significantly lower than those of both parental lines, and that of PYL 5 was marginally lower than that of IAS39 (Fig. 4b). VBNs of PYLs 5–7 were not significantly different from that of Asominori but were significantly lower than that of IR24. VBNs of PYLs 8–10 (qVBN5 + qVBN6) were 14.8–17.2, not significantly different from that of IAS39, but significantly lower than those of IAS30 and IR24 (Fig. 4c).

QTL detection in RILs

VBN is a quantitative trait controlled by multiple genes. Many QTLs for VBN in rice have been identified in segregating populations and by GWAS (Bai et al. 2012; Cui et al. 2003; Sasahara et al. 1999; Liao et al. 2021; Zhai et al. 2018; Zhang et al. 2002). Only QTLs with a major effect have been analyzed in detail, because their large phenotypic effect can mask minor QTLs (Collard et al. 2005; Yamamoto et al. 2009). RILs and DHs are useful for identification of QTLs with small effects across different environments (Collard et al. 2005; Kearsey and Farquhar 1998), because environmental influence can be minimized by using multiple replicates over multiple years. Sasahara et al. (1999) detected five QTLs for VBN in 3 years’ evaluation by using Asominori × IR24 RILs. They detected one QTL for VBN at 28.64 Mbp on Chr. 6 in all 3 years, one at 26.79 Mbp on Chr. 4 in 2 years, and one at 3.81 Mbp on Chr. 11 and two at 22.56 and 23.29 Mbp on Chr. 5 in 1 year each. Our QTLs correspond to the locations of previously reported QTLs in Sasahara et al. (1999) (Table 1). In addition, we detected stable QTLs with strong effects and high PVEs: qVBN5 and qVBN6 (detected in 3 years) and qVBN11 (detected in 2 years) had PVE values of 18.3–28.6%. Sasahara et al. (1999) detected two major QTLs for VBN on Chrs. 4 and 6 with PVEs of 20–23%. A significant proportion of QTLs affecting a trait are active across multiple environments, and highly heritable traits are more repeatable and stable across environments (Paterson et al. 1991; Tanksley 1993). Thus, qVBN5, qVBN6, and qVBN11 were stable owing to their large effects (PVE 18.3–28.6%) and frequent appearance (2–3 years). qVBN6 was stable and had a large effect, and qVBN5 and qVBN11 were stable but had a small effect. The variations in results between our study and Sasahara et al. (1999) are due to testing of RILs derived from the same parents in different environments, implying the influence of environmental factors on VBN.

The QTLs for VBN on Chrs. 5 and 11 might be new, because there are no reported such QTLs around these regions. In contrast, QTLs on Chrs. 4 and 6 were located near QTLs for VBN reported previously: on Chr. 4 at 31.07–31.26 Mbp (Zhai et al. 2018) and at 22.0–26.8 Mbp (Cui et al. 2003). Here, qVBN4 was located around 24.7 Mbp, near NAL1 (Os04g0615000) at around 31.2 Mbp on Chr. 4. NAL1 controls the leaf vein pattern and increases panicle neck diameter and VBN (Fujita et al. 2013; Qi et al. 2008). qVBN6 was located around 28.6 Mbp, near APO1 (Os06g0665400) at around 27.46 Mbp on Chr. 6. APO1 enhances the development of vascular bundles, promoting carbohydrate translocation to the panicle (Terao et al. 2010) and therefore they might house the same gene.

The phenotypic variation of each QTL detected in the RILs differed by year. qVBN5 and qVBN6 appeared in all 3 years, with PVEs of 10.3–28.6% and 13.6–18.3%, respectively; qVBN11 appeared in 2 years, with PVEs of 11.8–24.0%; and qVBN4 appeared only in 2018, with PVE of 10.4% (Table 1). The total PVE of the three QTLs detected in each year (44.4% in 2016, 66.2% in 2017, and 39.0% in 2018) cannot explain all of the difference in VBN between the parents. Other genetic factors might also control VBN.

Phenotypic validation of single QTL effect

The phenotypic effects of QTLs cannot be precisely confirmed through the use of RILs (Yamamoto et al. 2009), but can be determined through the use of CSSLs. As the genotypes of CSSLs are uniform and phenotypic variation is due mainly to the substitution segments carrying target QTLs, each QTL detected in the RILs could be validated after the evaluation of CSSLs in two years. VBNs showed similar tendencies between years, and only IAS14 and IAS39 not IAS30 were lower than that of IR24 (Fig. 2), implying a stable effect of these QTLs. The VBN of IAS39 carrying qVBN6 decreased from 6.6 to 8.1 compared to that of IR24. The VBN of IAS14 carrying qVBN11 decreased from 3.3 to 4.9 compared to that of IR24. The genetic background of each CSSL is ~ 93% similar to IR24 (Kubo et al. 2002). Thus, the reductions of VBN in each CSSL were caused by the presence of the Asominori chromosomal segment in the target region of each detected QTL. On the other hand, the effect of qVBN5 on IAS30 (carrying qVBN5) was similar to that of IR24. IAS30 was taller and had a thicker culm than IR24, to which we attribute the higher VBN. Lee et al. (1992) found a significant correlation between VBN, panicle length, and internode thickness. Therefore, peduncles on taller plants are thicker, and the plants have a higher VBN.

Confirmation of the detected QTLs in CSSLs

We conducted QTL analysis to confirm the causal regions related to decreased VBN in each CSSL carrying a QTL. We confirmed qVBN6 (27.02–27.65 Mbp) and qVBN11 (1.18–1.28 Mbp) in the regions detected in the RILs (Table 2). We detected two qVBN5 QTLs, qVBN5.1 and qVBN5.2, around 21.11–22.52 and 24.44–24.82 Mbp, respectively, close to the regions of two QTLs detected by Sasahara et al. (1999) around 22.56 and 23.29 Mbp. These results suggest that CSSLs have higher sensitivity for QTL detection and can accurately evaluate effects of a single QTL. In addition, we confirmed qVBN6 as a major QTL (PVE 61.9%) and qVBN5 and qVBN11 as minor QTLs (12.6–17.9%) for VBN in the RILs. These results will contribute to knowledge of the complex genetics of VBN in the panicle neck in rice and set the foundation for potential fine mapping and cloning of these loci.

Effects of pyramiding QTLs

To understand the interactions of QTLs related to VBN, we developed pyramided lines carrying pairs of QTLs. The VBNs of PYLs 1–4 (qVBN5 + qVBN11) and of PYLs 8–10 (qVBN5 + qVBN6) were the same as those of the parental lines IAS14 and IAS39, respectively. Based on our observations, plants of PYLs 1–4 and PYLs 8–10 were taller than IR24 owing to inheritance of the tall plant type from IAS30 (carrying qVBN5). Therefore, tall plant type might influence peduncle thickness and VBN. The VBNs of PYLs 5–7 (qVBN6 + qVBN11) were significantly less than those of both parental lines, so the effects of the two QTLs on VBN might be independent in the IR24 genetic background. In addition, the VBN of PYL 6 was 11.6, close to that of Asominori (11.2 VBN), whereas PYL 7 differed from those of both parental lines. This implies that introducing one or two QTLs might not be sufficient to increase VBN in the Asominori genetic background. Our studies found that a single QTL of qVBN11 and qVBN6 from IR24 allele in the genetic background of Asominori CSSLs increased VBN from 1–2 VBN (data not shown). It will be necessary to find several QTLs for VBN with minor effects to understand the genetic basis of the difference in VBN between indica and japonica rice cultivars.

QTL, quantitative trait locus; VBN, vascular bundle number; CSSL, chromosomal segment substitution line; RIL, recombinant inbred line; DH, double haploid line; GWAS, genome-wide association study; PYL, pyramided line.

Acknowledgements

We thank Prof Hideshi Yasui and Assoc Prof Yoshiyuki Yamagata, belong to Kyushu University, for providing experimental materials through the National Bio-resource project. Ha Thi Le Nguyen was funded by the Vietnamese government for “the critical program of biotechnology development and application in agriculture and rural development.” This research was part of the dissertation submitted by the first author in partial fulfilment of the Ph.D. degree. All authors have provided consent.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Ha Thi Le Nguyen, Shizuka Suetsugu, Yuna Nakamura and Zita Demeter. The manuscript was written by Ha Thi Le Nguyen, Shao Hui Zheng and Daisuke Fujita.

Data availability

Not applicable.

Ethics approval

All authors approved the submission. All authors declare no competing interests

Consent to participate

Not applicable.

Consent for publication

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Conflict of interest

The authors declare no competing interests.

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Identification and characterization of stable QTLs for vascular bundle number at the panicle neck in rice (Oryza sativa L.)

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Abstract

Figures

Introduction

Materials And Methods

Plant materials

Plant growth condition and evaluation of VBN

Extracting DNA and genotyping

DNA markers

QTL analysis

Results

Identification of QTLs for VBN in RILs

Validation of single QTL effects on VBN in IR24 genetic background

Confirmation of QTLs for VBN using CSSLs

Evaluation of pyramid effects of two QTLs for VBN in IR24 genetic background

Discussion

QTL detection in RILs

Phenotypic validation of single QTL effect

Confirmation of the detected QTLs in CSSLs

Effects of pyramiding QTLs

Abbreviations

Declarations

Acknowledgements

Funding

Competing interests

Author contributions

Data availability

Ethics approval

Consent to participate

Consent for publication

Conflict of interest

References

Supplementary Files

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