QTL Mapping by Whole Genome Re-sequencing and Analysis of Candidate Genes for Nitrogen Use Efficiency in Rice.

Nitrogen is a major nutritional element in rice production. However, excessive application of nitrogen fertilizer has caused severe environmental pollution. Therefore, development of rice varieties with improved nitrogen use efficiency (NUE) is urgent for sustainable agriculture. In this study, bulked segregant analysis (BSA) combined with whole genome re-sequencing (WGS) technology was applied to finely map quantitative trait loci (QTL) for NUE. A key QTL, designated as qNUE6 was identified on chromosome 6 and further validated by Insertion/Deletion (InDel) marker-based substitutional mapping in recombinants from F2 population (NIL-13B4 × GH998). Forty-four genes were identified in this 266.5-kb region. According to detection and annotation analysis of variation sites, 39 genes with large-effect single-nucleotide polymorphisms (SNPs) and large-effect InDels were selected as candidates and their expression levels were analyzed by qRT-PCR. Significant differences in the expression levels of LOC_Os06g15370 (peptide transporter PTR2) and LOC_Os06g15420 (asparagine synthetase) were observed between two parents (Y11 and GH998). Phylogenetic analysis in Arabidopsis thaliana identified two closely related homologs, AT1G68570 (AtNPF3.1) and AT5G65010 (ASN2), which share 72.3 and 87.5% amino acid similarity with LOC_Os06g15370 and LOC_Os06g15420, respectively. Taken together, our results suggested that qNUE6 is a possible candidate gene for NUE in rice. The fine mapping and candidate gene analysis of qNUE6 provide the basis of molecular breeding for genetic improvement of rice varieties with high NUE, and lay the foundation for further cloning and functional analysis.

Introduction

n class="Chemical">Nitrogen, onpan>e of the most demanpan>ding mineral elemenpan>ts in planpan>ts, is also the most commonpan> limiting factor for planpan>t growth in nature. pan> class="Chemical">Nitrogen deficiency can cause yellow leaves, dwarf plants, fewer tillers and lower grain yields during the process of rice growth and development. In agricultural production practices, substantial increases in nitrogen fertilizer application has been one of the important ways to increase rice yield (Mulvaney et al., 2009), and it results in agroecological environmental pollution and climate change etc. (Matson et al., 2002; Guo et al., 2010; Shen et al., 2014; Wang Y. et al., 2016). Therefore, there is an urgent need to understand the genetic mechanisms underlying NUE and improve rice varieties NUE via genotype selection (Zeigler and Mohanty, 2010; Mcallister et al., 2012).

The previous studies suggested that NUE is a quantitative trait, being controlled by major genes and minor efn class="Chemical">fects QTLs. The clonpan>ing anpan>d funpan>ctionpan>al anpan>alysis of some genpan>es or QTLs has provided anpan> importanpan>t basis for unpan>derstanpan>ding the molecular mechanpan>isms unpan>derlying pan> class="Species">rice NUE. Nitrate is the main inorganic nitrogen for its acquisition, transport, assimilation in plants. Members of nitrate/peptide (NTR/PTR) transporter family are the low-affinity nitrate transporters. NRT1.1, also known as CHL1, was initially identified in Arabidopsis thaliana (Tsay et al., 1993). In rice, Lin et al. (2000) cloned OsNRT1 gene and found that it was homologous to Arabidopsis thaliana AtNRT1 gene and encodes a low affinity nitrate transporter. Hu et al. (2015) showed that NRT1.1B had a nitrate-transporting activity at low and high nitrate ion concentrations. Another nitrate transporter, NRT2, is a high-affinity nitrate transporter but can't transfer independently. OsNAR2.1, a partner protein for the high-affinity nitrate transporter, is able to interact with OsNRT2.1, OsNRT2.2, and OsNRT2.3, and can enhance nitrate uptake by rice roots at different nitrate supply levels (Yan et al., 2011). Chen et al. (2017) indicated that rice uptake, yield and NUE were improved by increased OsNAR2.1 expression via its native promoter. Fan et al. (2016) found that OsNRT2.3b was able to increase the pH-buffering capacity of the plant, increasing the uptake of N, Fe, and P, improving NUE and grain yield.

In addition to n class="Chemical">nitrate, pan> class="Chemical">ammonium nitrogen is another main nitrogen source for plant growth. A large number of ammonium transporter (AMT) genes have been identified in rice. Among them, OsAMT1;1, OsAMT1;2, and OsAMT1;3 are the major ammonium transporters that absorb (Sonoda et al., 2003; Ferreira et al., 2015; Yang et al., 2015). Asparagine synthetase is the major enzyme that assimilates in rice. OsAS1 gene is responsible for expressing AS when ammonium is supplied to roots, being involved in primary assimilation of in roots (Ohashi et al., 2015). Furthermore, Sawaki et al. found that NIGT1 was a nitrate-induced but self-inhibited transcriptional repressor that played a pivotal role in the response of rice to nitrogen (Sawaki et al., 2013). Sun et al. (2014) mapped and cloned a major QTL for NUE in rice, qNGR9, which changed the response of rice to nitrogen by regulating the activity of a G protein. Zhang et al. (2015) identified a major QTL on chromosome 12, TOND1, and found that its over-expression enhanced the tolerance of rice to nitrogen deficiency.

In nature, a majority of agronomically important crop traits are quantitative (Paterson et al., 1988). QTL mapping is a highly efn class="Chemical">fective approach for genpan>etic dissection of quanpan>titative traits anpan>d provides a starting point for map-based cloning of related genpan>es anpan>d marker-assisted selection (Xu et al., 2015). However, the traditional QTL anpan>alysis is labor-consuming anpan>d costly (Salvi anpan>d Tuberosa, 2005). BSA has beenpan> applied to rapidly idenpan>tify the molecular markers closely linked with QTLs or genpan>es by genpan>otyping only two bulked DNA samples from two populations with 20–50 individuals from each, showing extreme opposite trait values for a givenpan> phenpan>otype in a segregating progenpan>y (Michelmore et al., 1991). With the developmenpan>t of the next-genpan>eration sequenpan>cing technology, WGS has beenpan> widely applied in genpan>otyping. By combining both BSA anpan>d WGS, QTLs for importanpan>t agronomic traits of crops have beenpan> rapidly idenpan>tified (Takagi et al., 2013). At the presenpan>t, manpan>y genpan>es anpan>d QTLs of planpan>ts have beenpan> mapped through QTL-seq (Takagi et al., 2013, 2015; Lianpan>g et al., 2016; Wanpan>g H. et al., 2016; Wanpan>g Y. et al., 2016; Panpan>dey et al., 2017; Song et al., 2017).

In this study, pools of low and high bulk samples (n = 30, each group) were constructed from 280 F2 individuals derived from the cross between n class="Chemical">GH998 (high NUE) anpan>d NIL-13B4 (low NUE anpan>d derived from “pan> class="Chemical">GH998 × Y11”) plants, and then used to detect the regions in the rice genome harboring major QTLs for NUE by BSA, WGS, and single nucleotide polymorphism index (SNP-index) methods. The results were further confirmeded by substitution mapping and qRT-PCR.

Materials and methods

Materials and phenotypic identification

In this study, the wild n class="Species">rice Y11 (six genpan>erationpan>s of single planpan>t bagging selfing) was selected as the pan> class="Species">donor and the elite rice variety GH998 was selected as the recipient. From the fall 2007, the methods of crossing, back-crossing and marker-assisted selection were adopted. A set of near isogenic lines (BC4F6) was bred. Then we selected the low NUE near-isogenic lines NIL-13B4 (8.9% genomes were derived from Y11; Supplementary Figure S1) to cross with GH998. In 2014, the F1 population was grown at the experimental station (Nanning, 22.85°N, 108.26°E) at Rice Research Institute of Guangxi Academy of Agricultural Sciences, was self-pollinated to generate F2 lines that were subsequently used as mapping populations. A total of 280 F2 individuals were selected and self-pollinated to generate 280 F2:3 families (Zhang and Xu, 2004). The soil in the experimental station is of weak acidity with pH = 6.48, containing 0.12% total phosphorus, 0.11% total phosphorus, 1.78% total potassium, 90.50 mg kg−1 available nitrogen, 34.00 mg kg−1 available phosphorus and 198.50 mg kg−1 available potassium. The field was assigned into groupI(urea, 0 kg.hm−2) and groupII(urea, 326.1 kg.hm−2). In addition, the potassium chloride at 166.7 kg.hm−2 and phosphorus pentoxide at 833.3 kg.hm−2 were applied in both groupI and groupII. Plants were transplanted in the plots of 20 m2 with the line spacing of 23.1 cm and plant spacing of 13.2 cm with three replicates.

After the rice grains had ripenpan>ed, 10 represenpan>tative plants for each replicate were cut alonpan>g the ground and their stems, leaves and seeds were collected and incubated for heating at 105°C for 30 min followed by incubationpan> for s at 75°C 4 day to conpan>stant weight. After being weighted using an electronpan>ic balance with resolutionpan> of 0.001 g, the total nitrogen content of plant was determined using semi-micro Kjeldahl method (Yoshida et al., 1976a). The NUE was calculated using the following equation (Wei et al., 2012):

TN0 is the total nitrogen conpan>tenpan>ts of plants in the nonpan>-nitrogen fertilizer treatment group;

TNF is the total nitrogen conpan>tenpan>ts of plants in the nitrogen fertilizer treatment group;

N is the total amount of n class="Chemical">nitrogen applied.

DNA isolation and analysis of WGS data

Youngth and healthy leaves of two parents and F2 individuals were collected at the tillering stage and stored at −80°C freezer. Their genomic DNA was extracted using n class="Chemical">CTAB method with modificationpan> (Murray anpan>d Thompsonpan>, 1980). The purity anpan>d integrity of each DNA sample were determined by pan> class="Chemical">agarose gel electrophoresis. DNA samples of 30 plants with extremely lowNUE were mixed with equal amounts and used as L-pool (30 F2 progeny NUE range from 3.6 to 9.8%) and DNA samples of 30 plants with extremely high NUE were mixed with equal amounts and used as H-pool (30 F2 progeny NUE range from 26.8 to 34.4%). DNA samples isolated from Y11 and GH998 plant leaves, and the two DNA pools, were randomly fragmented using a covaris crusher to 350 bp, and subjected to terminal repair, polyA tailing, sequencing adaptor ligation, purification and PCR amplification. The sequencing libraries were constructed, which were sequenced on the IlluminaHiSeq2500 platform with sequencing depth of 20× for parental plants and 30× for each pool.

The calculation of SNP-index is a statistical method for SNP in the pool. The principle is that the sequencing reads are used to statistically count the number of reads at certain each base locus with same or difn class="Chemical">ferenpan>t bases to onpan>e of its parenpan>t or repan> class="Chemical">ference genome and the percentages of reads with different bases are caculated, that is, the SNP-index at this base locus. For projects with two sub-pool data, the loci with SNP-index < 0.3 in both pools were filtered out. The average of the filtered SNP-index for 5 kb bases at a window of 50 kb is considered as the SNP-index of the window. The SNP-index of the two pools is calculated according to the above method, and their difference is calculated as the Δ(SNP-index). The difference in SNP-index between the two pools is calculated as Δ(SNP-index) = SNP-index (extreme trait B) -SNP-index (extreme trait A). A thousand replacement tests are performed and the 95% confidence level is selected as the threshold for screening. At the 95% confidence level, a window larger than the threshold is selected as the candidate interval (Takagi et al., 2013).

QTL analysis with SSR and InDel marker

A total of 24 simple sequence repeat (SSR) markers covering the chromosome 6 were used for mapping genotypes in the 60 F2 individuals and gained nine recombinants between the two markers RM539 and RM136 (Supplementary Table S1). These recombinants were used for substitutional mapping with InDel markers (Maeda et al., 2014; Oikawa et al., 2015). Additional InDel markers were designed by us. In detail, the genome sequence of n class="Species">Oryza sativa L. ssp. pan> class="Species">japonica cv. Nipponbare (ftp://ftp.ensemblgenomes.org/pub/plants/release-30/fasta/oryza_sativa/Dna/) was used as a reference sequence, and the WGS sequences of Y11 and GH998 were compared using software BWA to find out the InDel difference between the two parents (Y11 and CH998). In addition, the 500 bp sequence framing around the loci were extracted using the self-made pearl language script and the InDel markers with good polymorphism and strong specificity between the two varieties and the two pools were designed using software Primer 5.

Candidate genes and gene ontology (GO) enrichment analysis

Gene prediction and annotation within the 8,647,275–8,913,783 bp on the QTL region of chromosome 6 were performed using MSU-RGAP (http://n class="Species">rice.planpan>tbiology.msu.edu/). Based onpan> the WGS data of Y11 anpan>d pan> class="Chemical">GH998, ANNOVAR was used to detect and annotate SNPs or InDels (Wang et al., 2010). Genes with SNPs causing stop gain or loss, non-synonymous and splicing (the introns are close to exons or intron boundary 2 bp), with InDels causing stop gain or loss, frameshift mutation in their corresponding alleles were selected as the candidate genes. Meanwhile, genes with SNPs or InDels in the promoter region (≤1 kb) from the start codon ATG in their corresponding alleles were also selected as the candidate genes. All candidate genes were analyzed by GO enrichment analysis (http://www.geneontology.org/) based on a Fisher's exact test and a Yekutieli multitest adjustment, using a 5% false positive detection threshold.

Analysis of expression levels of candidate genes by qRT-PCR

The seeds of n class="Chemical">GH998, Y11 anpan>d NIL-13B4 with the same germinationpan> status were selected. They were cultured first in double distilled pan> class="Chemical">water adjusted to pH 5.5 with MMES-NaOH at 28°C in a light incubator (13 h light/11 h dark) to three-leaf stage, and then in normal nutrient solution (+N,1 mM NH4NO3) (Yoshida et al., 1976b). Samples of leaves and stem were collectedat after 48 h. Total RNA was extracted with Trizol method (Invitrogen, Carlsbad, CA, USA). Reverse transcription was performed by One-Step gDNA removal and cDNA synthesis superMix (TransGen, Beijing, China). The Actin3 was used as normalization of expression levels of candidate genes with primers F: CCACTATGTTCCCTGGCATT and R: GTACTCAGCCTTGGCAATCC (Sun et al., 2014). The qRT-PCR reaction system was consisted of 2× TransStart SYBR Green Master Mix 10 μl, the forward primer 1 μl, the reverse prime1 μl, template cDNA 1 μl, and double distilled water was added to 20 μl. Reaction program was set as follows: pre-denaturation at 94°C for 5 min, folowed by 35 cycles of 30 s at 94°C, 30 s at 55°C, 1 min at 72°C. The qRT-PCR analysis was performed on AnalytikJena qTOWERE2.2 (AnalytikJena, Germany).

Phylogenetic analysis of qNUE6

MSU-RGAP (http://n class="Species">rice.planpan>tbiology.msu.edu/) conpan>tains informationpan> about canpan>didate genpan>es for orthologous genpan>es in the other planpan>ts. The homologous genpan>e of pan> class="Species">Arabidopsis thaliana were downloaded from TAIR (http://www.arabidopsis.org/index.jsp), while other plants genes were downloaded from Phytozome (https://phytozome.jgi.doe.gov/pz/portal.html). Protein sequence alignment was performed using ClustalW with default parameters. Phylogenetic trees were constructed by MEGA7.0 using Maximum Likelihood method with 1,000 bootstrap replications.

Results

Statistical analysis of phenotypes of NUE

The NUE was evaluated for each F2:3 families and for two lines (n class="Chemical">GH998 anpan>d NIL-13B4). The average NUE in each individual in F2:3 families represenpan>ts the NUE in its corresponding F2 individual (Zhanpan>g anpan>d Xu, 2004). As a result, the NUE of the 280 F2 lines were fluctuated in the ranpan>ge of 3.64–34.39% with the maximum efficienpan>cy being 9.32 times of the minimum efficienpan>cy, the meanpan> efficienpan>cy of 18.05, the stanpan>dard deviation of 6.97, anpan>d the normality test value of 0.9821, n class="Species">indicating that the phenotype of NUE in F2 population is accorded with normal distribution (Figure 1).

Figure 1

Frequency distribution of nitrogen use efficiency in the F2 population.

Frequency distribution of n class="Chemical">nitrogen use efficienpan>cy in the F2 population.

QTL for NUE identified by QTL-seq

Genomic DNA samples of two parents (GH998 and Y11) and the two pools (H-pool and L-pool) were sequenpan>ced by IlluminaHiSeq 2500 sequenpan>cer and 53.68 Gb raw data were genpan>erated. After being filtered, 53.0 GB clean data were obtained (Table 1). These data were enpan>d paired and those data with adapter, or nitrogen content exceeding 10% of the total length, more than 50% of bases with quality score Q ≤ 5 were removed.

Table 1

The quality of sequencing data.

Sample	Raw base(bp)	Clean base(bp)	Effective rate(%)	Error rate(%)	Q20(%)	Q30(%)	GC content(%)
Y11	6,867,532,750	6,808,586,750	99.19	0.04	94.48	89.51	42.94
GH998	6,992,487,000	6,925,416,500	99.14	0.04	94.69	89.94	43.38
L-pool	18,695,867,000	18,536,966,000	99.2	0.04	94.64	89.89	43.32
H-pool	21,128,305,000	20,732,080,750	98.12	0.06	92.24	86.2	45.59

The sequence data were compared with the ren class="Chemical">ferenpan>ce genpan>ome of pan> class="Species">Oryza sativa L. ssp. japonica cv. Nipponbare. GH998 had 55,403,332 valid reads, covering 97.76% of the whole genome at average read depth of 17.97×. Y11 had 52,786,428 valid reads, covering 96.91% of the whole genome with average read depth of 17.79× (Table 2). Based on genotyping results, a total of 1,054,326 polymorphic markers were screened from the homozygous SNPs of GH998 and Y11. The SNP-index (the frequency of SNP) of the progeny markers between two parents was calculated using the parental GH998 as a reference genome. Among them, the exact same progeny marker had SNP-index of 0 and the totally different progeny marker had SNP-index of 1 (Takagi et al., 2013). To intuitively reflect the distribution of the progeny SNP-index on the chromosome, the distribution of SNP-index on the chromosome is plotted using 50 kb as the window and average SNP-index of every step of 5 kb. By comparing the SNP-index of low and high NUE pools and analyzing the window above the threshold at 95% confidence level, we found an unbalanced SNP between 6,099,043–8,940,631 bp on chromosome 6 (Figure 2). In this region, the SNP-index of the low pool was greater than or equal to 0.7, and the high pool was lower than or equal to 0.3. These results indicated that the low pool individuals contained the same fragment of Y11 were between 6,099,043 and 8,940,631 bp on chromosome 6 of rice. The high pool individual contained the same fragments of GH998 in the same region. Meanwhile, the Δ(SNP-index) of this region was greater than the threshold at 95% confidence level. Therefore, the region from 6099043 to 8940631 bp is likely the locus controlling NUE in rice, which was named qNUE6.

Table 2

Statistical analysis of sequencing depth and coverage.

Sample	Mapped reads	Total reads	Mapping rate(%)	Average depth(×)	Coverage at least 1 × (%)	Coverage at least 4 × (%)
Y11	52,786,428	54,468,694	96.91	17.79	86.61	78.83
GH998	54,162,109	55,403,332	97.76	17.97	88.72	83.07
L-pool	161,503,688	165,856,646	97.38	50.27	93.19	89.46
H-pool	144,233,962	148,295,728	97.26	45.2	92.38	88.81

Figure 2

SNP-index graphs of L-pool (A), H-pool (B), and Δ(SNP-index) graph (C) from QTL-seq analysis. X-axis represents the position of 12 chromosomes; Y-axis represents the SNP-index. Major QTL is located to chromosome 6.

Validation of NUE QTL by substitutional mapping

In order to validate the QTL for NUE qNUE6, we performed a genome survey using SSR markers covering the chromosome 6. By analysis, the QTL qNUE6 was narrowed between markers RM539 and RM136 (Supplementary Table S2). We also found nine recombinants between the two markers RM539 and RM136. Subsequently, we designed 20 pairs of InDel markers based on the re-sequencing results of Y11 and n class="Chemical">GH998. Amonpan>g them, 11 pairs of Inpan>Dels markers were founpan>d to have good polymorphism anpan>d stronpan>g specificity (Supplemenpan>tary Table S3). The nine recombinanpan>ts were used for substitutionpan> mapping of qNUE6. The results indicated that qNUE6 was narrowed down to a 266.5-kb region between markers ID10 and ID22 (Figure 3), which had a physical distance from 8,647,275–8,913,783 bp on chromosome 6.

Figure 3

Identification and validation of nitrogen use efficiency QTL qNUE6 on rice chromosome 6. Nine recombinant individuals in the F2 population were used for substitution mapping of qNUE6, which was narrowed down to a 266.5-kb region between the markers ID10 and ID22.

Identification and validation of nitrogen use efficienpan>cy QTL qNUE6 onpan> rice chromosome 6. Nine recombinant individuals in the F2 population were used for substitution mapping of qNUE6, which was narrowed down to a 266.5-kb region between the markers ID10 and ID22.

Candidate genes and expression analysis

There are 44 predictive genes (http://n class="Species">rice.planpan>tbiology.msu.edu/) in the regionpan> from 8,647,275 to 8,913,783 bp onpan> chromosome 6 (Supplemenpan>tary Table S4). Amonpan>g these difpan> class="Chemical">ferent loci which contained the candidate region between Y11 and GH998, 692 large-effect SNPs and 20 large-effect InDels were found in a total of 39 genes (Supplementary Tables S5, S6). These genes were identified by GO enrichment analysis in each of the three maincategories (biological processes, molecular function and cellular components). In the significantly enriched GO terms, these terms are involved in biological process (Figure 4), including asparagine metabolic process (GO: 0006528) and asparagine biosynthetic process (GO: 0006529), and TERM asparagine synthetase (glutamine-hydrolyzing) activity (GO: 0004066).

Figure 4

Significantly enriched GO terms of the genes involving SNP or InDel variations.

We examined the expression levels of 39 genes in root and stem-leaf using qRT-PCR. Based on the cDNA sequences, we designed 39 primers pairs for qRT-PCR analysis (Supplementary Table S7). The qRT-PCR results showed that no expression levels of 11 genes were detected in root and stem-leaf of Y11 and n class="Chemical">GH998. Amonpan>g the 28 genpan>es of normal expressionpan>, the expressionpan> levels of LOC_Os06g15370 genpan>e in the root anpan>d stem-leaf of Y11 were signpan>ificanpan>tly lower thanpan> those in root anpan>d stem-leaf of pan> class="Chemical">GH998. Similarly, the expression level of LOC_Os06g15420 in root of Y11was significantly lower than that in GH998, but not obvious expression profile was observed in stem-leaf (Figure 5; Supplementary Figure S2).

Figure 5

Relative expression of LOC_Os06g15370 (A) and LOC_Os06g15420 (B) after 48 h of trearment with 1 mM NH4NO3 nutrient solution in GH998 and Y11. The X-axis represents different treatment stage; the Y-axis are scales of relative expression level. Error bars indicate standard deviations of independent biological replicates.

Relative expression of LOC_Os06g15370 (A) and LOC_Os06g15420 (B) after 48 h of trearment with 1 mM n class="Chemical">NH4NO3 nutrienpan>t solutionpan> in pan> class="Chemical">GH998 and Y11. The X-axis represents different treatment stage; the Y-axis are scales of relative expression level. Error bars indicate standard deviations of independent biological replicates.

Phylogenic analysis of qNUE6

LOC_Os06g15370 encodes peptide transporter (n class="Gene">PTR2) anpan>d its conpan>served domain was anpan>alyzed using Batch CD-search (https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi). We founpan>d that both LOC_Os06g15370 anpan>d pan> class="Gene">AT1G68570 in Arabidopsis thaliana shared a protein domain of the PTR2 super family. AT1G68570 was shown to encode a chloroplastic nitrite transporter (Sugiura et al., 2007). AtNPF3.1 (AT1G68570) was found to be a low-affinity transporter for both nitrate and nitrite, displaying the biophysical characteristics of the known NPF transporters (Pike et al., 2014). Phylogenetic analysis in graminaceous crops revealed that both GRMZM2G361652 and Sb10g009530 shared 86.2 and 86.4% similarity in amino acid sequences with LOC_Os06g15370, respectively. The GRMZM2G361652 (PTHR11654:SF178, http://www.pantherdb.org/) in maize putatively encodes nitrite transporter, while Sb10g009530 (PTHR11654:SF178, http://www.pantherdb.org/) in sorghum encodes nitrate transporter, and both nitrite transporter and nitrate transporter have a domain of PTR2 super family proteins (Supplementary Figure S3).

LOC_Os06g15420 (OsAS2) encodes n class="Gene">asparagine synthetase (AS). The results of the study by Ohashi et al. (2015) indicated that OsAS2 mRNA was detectable in the roots, but its expression level was decreased when was supplied. In Arabidopsis thaliana, AT5G65010 (ASN2) is correlated to ammonium metabolism in higher plants (Wong et al., 2004; Gaufichon et al., 2013), which shares 87.5% similarity in amino acid sequences with LOC_Os06g15420. OsAS2, ASN2, and ASN3 all have a characteristic domain of the protein members in GlmS super family. Phylogenetic analysis in graminaceous crops, indicated that GRMZM2G074589 and Sb10g009590 shared 94.4 and 95.6% similarity in amino acid sequences with LOC_Os06g15420, respectively (Supplementary Figure S4).

Evaluation of important agronomic traits of near-isogenic lines of QTL qNUE6 for NUE in rice

In 2016, heading date, culm length, tiller number, grain number per panicle, 1000-grain weight, and individual yield of NIL-13B4, NIL-13B33 (high NUE and contains qNUE6) and n class="Chemical">GH998 were measured. The results showed that number of tillers was not signpan>ificanpan>tly difpan> class="Chemical">ferent among GH998, NIL-13B33, and NIL-13B4, whereas the difference in heading date among them reached a highly significant level and the differences in culm length, 1000-grain weight and single-individual yield among them reached a significant level (Supplementary Table S8).

Discussion

With the rapid development of molecular biology and genomics, more and more breeders have paid their attention to molecular breeding in plants. Molecular plant breeding can realize the direct selection and efn class="Chemical">fective pyramiding of genpan>es, increase breeding efficienpan>cy anpan>d shortenpan> the breeding cycles (Mumm anpan>d pan> class="Species">Moose, 2008). In plants, gene mapping, cloning and functional analysis are the foundations for molecular plant breeding. Although many NUE genes or QTLs have been mapped or cloned, the regulatory mechanisms underlying NUE are still complicated, and NUE improvement in rice varieties breeding is very limited (Li et al., 2017). Therefore, we also need to identify new valuable NUE-related QTLs or genes for rice NUE breeding.

In this study, the QTL qNUE6 were finely mapped by BSA combined with WGS. By further using substitutional mapping with InDel markers, we delimited this QTL in 8,647,275–8,913,783 bp region on chromosome 6. In previous study, researchers have identified several genes or QTLs for NUE on chromosome 6. Song et al. (1996) isolated and identified an aspartate transaminase gene AspAT3 around 23738029–23738154 bp, which is involved in n class="Chemical">carbon anpan>d pan> class="Chemical">nitrogen metabolism in rice. Shan et al. (2005) identified a nitrogen utilization QTL qNUEp-6 around Waxy gene in the 1.76–2.09 Mb region. Tong et al. (2006) detected QTLs related to above ground dry weight and yield within the 28.13–29.63 Mb region under normal and low nitrogen conditions. Wang et al. (2009) detected a novel QTL of effective panicle and yield in the range of 2.29 (RM587)–2.83 Mb (RM510) using rice chromosome fragment segment substitution lines (CSSLs). Liu et al. (2016) used 157 SSR markers to perform genome-wide association analysis of the nitrogen utilization traits of 184 rice cultivars and identified a novel NUE-related locus at SSR marker RM314 (4,845,258–4,845,375 bp). However, these genes or QTLs are not the same loic as qNUE6, which is a new QTL.

The region delimited by InDel marker ID10 and ID22 in the ren class="Chemical">ferenpan>ce genpan>ome pan> class="Species">Oryza sativa L. ssp japonica cv. Nipponbare was predicted to contain 44 genes. Of which, 39 genes were identified as candidates for association with NUE based on large-effect SNPs and large-effect InDels. Among the 39 candidate genes, the expression pattern of LOC_Os06g15370 and LOC_Os06g15420 suggested that they might be the candidate genes for NUE in rice. The expression level of LOC_Os06g15370 was identified significantly lower in Y11than in GH998 by qRT-PCR. It encodes peptide transporter and is highly homologous to the AtNPF3.1 gene in Arabidopsis thaliana, in which, AtNPF3.1 was found to similarly transport both nitrate and nitrite with low affinity (Pike et al., 2014). Zhao et al. (2010) analyzed 84 PTR family members in rice (OsPTR), and found that the orthologous OsPTR and AtPTR genes showed the expression profile similar to those of Os06g15370 and AT1G68570. Phylogenetic analysis in graminaceous crops revealed that both GRMZM2G361652 and Sb10g009530 shared high similarity in amino acid sequences with LOC_Os06g15370, which is a NRT1/PTR FAMILY 3.1 gene (https://phytozome.jgi.doe.gov/pz/portal.html). In rice, three PTR genes have been functionally verified. LOC_Os03g13274 (OsNRT1) encodes low-affinity nitrate transporter and was found to be expressed in stem, cuticle and root hairs (Lin et al., 2000). The results of a study by Hu et al. (2015) showed that variations in the expression of NRT1.1B (LOC_Os10g40600) largely explained nitrate-use divergence between indica and japonica and that NRT1.1B-indica could potentially improve the NUE of japonica. SP1 (LOC_Os11g12740) is located at the same locus with OsNPF4.1, which determines the panicle size (Li et al., 2009). Phylogenetic analysis implied that SP1 might be a nitrate transporter. However, neither nitrate-transporting activity nor the transporting activities for other compounds could be obtained from it (Léran et al., 2014).

n class="Gene">Asparagine synthetase plays importanpan>t roles in pan> class="Chemical">nitrogen metabolism and improvement of NUE in plants (Lam et al., 2003; Gaufichon et al., 2010). In this study, we observed that the expression levels of an asparagine synthetase gene, LOC_Os06g15420, were different between Y11 and GH998 in root. It was identified to be related to nitrogen assimilation in the qNUE6 region. Some asparagine synthetase genes have been found to play an important role in the nitrogen metabolism pathway in maize, Arabidopsis thaliana and soybean (Wong et al., 2004; Wan et al., 2006; Cañas et al., 2009; Gaufichon et al., 2013; Han et al., 2015). In phloem sap of rice, glutamine synthetase is the main form of transporter for nitrogen molecules, followed by asparagine synthetase (Hiroaki Hayashi, 1990). Nakano et al. (2000) found that the expression levels of OsAS varied in different tissues and different developmental stages of rice and might be closely related to nitrogen assimilation and translocation. Ohashi et al. (2015) showed that both OsAS1 and OsAS2 encoded asparagine synthetase and that OsAS1 was responsible for the initial assimilation of in root system while OsAS2 was mainly expressed in rice leaves and leaf sheaths. However, their biological functions in leaves are not clear yet.

Moreover, we identified the agronomic traits between near isogenic lines NIL-13B4 and NIL-13B33 by using the parent n class="Chemical">GH998 as the conpan>trol group. The results showed that QTL qNUE6 might have signpan>ificanpan>t efpan> class="Chemical">fect on heading date, culm length, 1000-grain weight and individual yield (Supplementary Table S8). Taken together, all of these results lead us to postulate that qNUE6 can be one of the important candidate genes for NUE in rice.

Conclusions

In this study, a QTL for NUE, qNUE6, was finely mapped through QTL-seq at the region from 8,647,275 to 8,913,783 bp on chromosome 6. LOC_Os06g15370 and LOC_Os06g15420 were identified as the candidate genes using gene annotation information, mutation site functional annotation and qRT-PCR. The qRT-PCR analysis showed that the expression levels of LOC_Os06g15370 and LOC_Os06g15420 were significantly difn class="Chemical">ferenpan>t betweenpan> two parenpan>ts. Phylogenpan>etic anpan>alysis revealed that the amino acid sequenpan>ces of LOC_Os06g15370 anpan>d LOC_Os06g15420 were highly homologous to those of pan> class="Gene">AtNPF3.1 gene and ASN3 gene in Arabidopsis thaliana, respectively. The analysis of important agronomic traits in near isogenic lines showed that qNUE6 might have significant effects on culm length, 1000-grain weight, individual yield, and especially on heading date. However, it is still not clear how qNUE6 affects the agronomic traits of rice and its biological function has not been further elucidated yet. Thus, further study is needed to elucidate its molecular and biological functions by cloning and transgenic approaches. The identification of qNUE6 as one of the important candidates for NUE provides an important genetic basis for the improvement of rice varieties with high NUE.

Accession codes

The sequence data has been deposited in National Center for Biotechnology Information (SRA): SRR5739119, SRR5739120, SRR5739121, SRR5739122, and SRR5739123.

Author contributions

XY designed and performed the experiment, and wrote the manuscript; XX performed the experiment and drew the graphs; BN, ZZ, and YZ performed the experiment; FX, YW, JG, and GD collected and analyzed data; DL designed and revised the manuscript; all the authors reviewed and approved this submission.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.