Transcriptomics-assisted quantitative trait locus fine mapping for the ...

Plant, Cell & Environment (2016) 39, 1601–1618

doi: 10.1111/pce.12731

Original Article

Transcriptomics-assisted quantitative trait locus fine mapping for the rapid identification of a nodulin 26-like intrinsic protein gene regulating boron efficiency in allotetraploid rapeseed Yingpeng Hua, Didi Zhang, Ting Zhou, Mingliang He, Guangda Ding, Lei Shi & Fangsen Xu

National Key Laboratory of Crop Genetic Improvement, Microelement Research Centre, Huazhong Agricultural University, Wuhan 430070, China

ABSTRACT Allotetraploid rapeseed (Brassica napus L., AnAnCnCn, 2n = 4x = 38) is extraordinarily susceptible to boron (B) deficiency, a ubiquitous problem causing severe losses in seed yield. The breeding of B-efficient rapeseed germ plasm is a cost-effective and environmentally friendly strategy for the agricultural industry; however, genes regulating B efficiency in allotetraploid rapeseed have not yet been isolated. In this research, quantitative trait locus (QTL) fine mapping and digital gene expression (DGE) profiling were combined to identify the candidate genes underlying the major-effect QTL qBECA3a, which regulates B efficiency. Comparative phenotype analyses of the near-isogenic lines (NILs) indicated that qBEC-A3a plays a significant role in improving B efficiency under B deficiency. Exploiting QTL fine mapping and DGE analyses revealed a nodulin 26-like intrinsic protein (NIP) gene, which encodes a likely boric acid channel. The gene coexpression network for putative B transporters also highlighted its central role in the efficiency of B uptake. An integration of whole-genome re-sequencing (WGS) with bulked segregant analysis (BSA) authenticated the emerging availability of QTL-seq for the QTL analyses in allotetraploid rapeseed. Transcriptomics-assisted QTL mapping and comparative genomics provided novel insights into the rapid identification of quantitative trait genes (QTGs) in plant species with complex genomes. Key-words: Brassica napus; near-isogenic lines; sequencing; positional cloning; boron transporter.

INTRODUCTION Boron (B) is an essential micronutrient for the normal growth and development of vascular plants (Warrington 1923). Soils with low available B are prevalent throughout the world, and severe crop failures caused by B deficiencies have occurred in more than 80 countries and involved 132 crops (Shorrocks 1997; Goldbach et al. 2001). Correspondence: F. Xu. Fax: +86 27 87280016; e-mail: fangsenxu@mail. hzau.edu.cn © 2016 John Wiley & Sons Ltd

Boron in vascular plants is principally involved in the formation and structural integrity of the primary cell wall by crosslinking pectic polysaccharide rhamnogalacturonan II (RG-II) (O’Neill et al. 2001; Bolaños et al. 2004). B deficiency primarily restrains developing tissues, including the inhibition of root elongation and leaf expansion, and abortion of pollen fertility, which ultimately leads to considerable crop yield losses and a dramatic decline in quality (Shorrocks 1997; Lordkaew et al. 2011). Allotetraploid rapeseed (Brassica napus L., AnAnCnCn, 2n= 4x = 38), which is formed by natural hybridization between Brassica rapa (ArAr, 2n = 2x = 20) and Brassica oleracea (CoCo, 2n = 2x = 18) (Chalhoub et al. 2014), is widely cultivated and the world’s second leading crop source of vegetable oil (following soybean) for human consumption (Fu 2004; Meyer 2009). Among various crop species, B. napus is extremely sensitive to B deficiency (Marschner 1995) and will exhibit flowering without seed setting during reproductive development when exposed to B limitation conditions (Wang et al. 2007). In recent years, numerous effective measures have been taken to address this problem, including the application of borate fertilizers to soils with low B abundance (Wang et al. 2007). However, borate rock is a depletable and non-renewable mineral resource. Moreover, among all of the essential mineral elements, the most narrow soil concentration margin occurs between B deficiency and B toxicity (Goldberg 1997). Thus, developing new oilseed rape germ plasm resources that present high B efficiency in B-deficient soils represents a promising and cost-effective strategy that is also sustainable and environmentally friendly for the agricultural industry over the long term. Boron in soils is primarily found in the form of uncharged boric acid (H3BO3) (Marschner 1995). Because B is not easily re-translocated from mature to developing organs, plants must constantly uptake B from soils and deliver it to growing tissues (Brown & Shelp 1997). Currently, influx boric acid channels and B efflux transporters in Arabidopsis have been identified and functionally characterized, with certain channels identified as indispensable for efficient B uptake and translocation under B-limited conditions (Miwa & Fujiwara 2010). In Arabidopsis, the influx of B from soils into root cells occurs through the boric acid channel gene AtNIP5;1, and the distribution of B into young growing tissues is facilitated by the B transporter gene AtNIP6;1, and both of these genes are up-regulated under B 1601

1602 Y. Hua et al. deficiency (Takano et al. 2006; Tanaka et al. 2008). Hence, regulating the transcript levels of these B transporters is crucial for maintaining B homeostasis in plants under B limitation conditions (Miwa & Fujiwara 2010). In addition, the first effluxtype transporter AtBOR1 has been identified as responsible for B loading toward the xylem out of root cells in Arabidopsis (Takano et al. 2002). However, under B-limited conditions, although AtBOR1 mRNA is not abundantly accumulated, its protein is fairly abundant in the plasma membrane (PM) (Takano et al. 2005). The B exporter BOR2 is the most similar paralog of BOR1 and is required for the effective cross-linking of RG-II and root elongation under B limitation conditions in Arabidopsis (Miwa et al. 2013). Additionally, the expression of the Arabidopsis borate efflux transporter gene, AtBOR4, in rice affects the xylem loading of B and tolerance to excess B (Kajikawa et al. 2011). Two recent studies in maize revealed that tassel less1 (tls1) encodes an aquaporin (AQP) family member gene orthologous to AtNIP5;1, whereas rotten ear (rte) encodes a B transporter gene orthologous to AtBOR1. The mutants derived from disrupting either gene promote decreased tassel development, inflorescence meristem defects, and vegetative defects in low-B soils (Chatterjee et al. 2014; Durbak et al. 2014). OsNIP3;1 is a rice boric acid channel that regulates B distribution and is essential for growth under Bdeficient conditions (Hanaoka et al. 2014). Although the principles underlying the uptake, transport and redistribution of B in Arabidopsis and other agriculture crops, such as maize and rice, are increasingly well understood, the genetic bases and molecular mechanisms underlying B efficiency in other crops, including B. napus, remain poorly elucidated. In recent years, the quantitative trait loci (QTLs) for B efficiency in B. napus, including BE1 and BnBE2, have been characterized by using several mapping populations (Xu et al. 2001; Zhao et al. 2008; Zhao et al. 2012). However, these previous QTL mappings were based on genetic linkage maps with low-density molecular markers. Because high-density genetic linkage maps can improve the precision of QTL localization (Stange et al. 2013), single-nucleotide polymorphisms (SNPs) genotyped by the Brassica 60K Infinium SNP array (developed by the international Brassica SNP consortium and Illumina Inc., San Diego, CA, USA) were used to construct a high-density genetic linkage map in a newly developed doubled haploid (DH) population derived from a cross between B-efficient (B-deficiency tolerant) Qingyou10 (QY10) as the donor and B-inefficient (B-deficiency sensitive) Westar10 (W10) as the recurrent parent (Zhang et al. 2014). Notably, the qBEC-A3a QTL was identified under B-deficient conditions, and it explains 30.79% of the phenotypic variance. Based on the substitution mapping, the QTL confidence interval was tentatively narrowed down to the genetic region between the markers CNU384 and BnGMS436 by a set of near-isogenic lines (NILs) (Zhang et al. 2014). Thus, fine-scale mapping and gene cloning of the major effect QTLs underlying B efficiency can be considered as prerequisites towards achieving an in-depth understanding of the genetic bases and molecular mechanisms underlying B uptake and transport in B. napus and used to develop B-efficient germ plasm for the agricultural industry.

In this research, a set of NILs that were previously derived from QY10 and W10 (Zhang et al. 2014) were used in the fine mapping of the major-effect QTL qBEC-A3a, which regulates B efficiency. Firstly, a segregation analysis of qBEC-A3a for B efficiency was conducted. Whole-genome re-sequencing (WGS) and bulked segregant analysis (BSA) were combined to authenticate the emerging availability of QTL-seq for QTL analyses of allotetraploid rapeseed and dozens of molecular markers tightly linked to the target QTL were developed. Comparative phenotype analyses of NILs demonstrated the pivotal role of qBEC-A3a in enhancing B efficiency in B. napus under B deficiency. Recombinants were identified by using the molecular markers flanking the QTL intervals in BC4F3 and BC4F4 populations consisting of 1349 and 2049 individuals, respectively. Stepwise substitution mapping delimited qBECA3a to a genomic region that spanned 119 kb and was flanked by the markers A03-66 and A03-71. Genome-wide differential transcriptional profiling of the roots and leaves of 10 d-old B. napus seedlings exposed to B limitations were characterized among the B-efficient parent QY10, B-inefficient parent W10 and B-efficient NILs NILQ, and the results were subjected to the analyses of the differentially expressed genes (DEGs) between B-efficient and B-inefficient lines. Combining fine mapping of qBEC-A3a and digital gene expression (DGE) profiling analyses, we were able to rapidly identify a nodulin 26-like intrinsic protein (NIP) gene that is homologous to AtNIP5;1 and a likely boric acid channel responsible for regulating B efficiency in B. napus. Allelic polymorphisms in the 5′ untranslated region (UTR) may represent a major contributor to differential expression of the candidate gene BnaA3. NIP5;1 and further contribute to B efficiency variations in B. napus genotypes and, potentially, other plant species.

MATERIALS AND METHODS Plant materials Homozygous NILs (BC4F2) with contrasting B efficiencies developed from the donor QY10 (B efficient) and the recurrent parent W10 (B inefficient) (Zhang et al. 2014) were used to construct the B-efficient and B-inefficient DNA bulks for WGS, which was further used to identify SNPs for the QTL-seq analysis and develop insertion/deletion (InDel)based molecular markers tightly linked to the target QTL. Derived from a heterozygous NILs (Supporting Information Fig. S1a) in the BC4F2 population, the BC4F3/BC4F3:4 and BC4F4/BC4F4:5 populations were used to fine map the major QTLs for B efficiency by using a progeny testing strategy. NILQ and NILW represent NILs carrying the homozygous alleles from QY10 and W10 in the QTL region harbouring qBECA3a, respectively, and they were used to assess the genetic effect of qBEC-A3a on the tolerance to B deficiency in B. napus and delineate DGE profiling of genes within the QTL region.

Phenotypic assessment Plump seeds of a similar size were surface sterilized and then germinated on a piece of moist gauze immobilized on a black © 2016 John Wiley & Sons Ltd, Plant, Cell and Environment, 39, 1601–1618

Fine mapping a boron efficiency gene in rapeseed 1603 plastic tray filled with ultrapure grade water (18.25 MΩ· cm). Uniform B. napus seedlings with similar hypocotyl and root lengths and cotyledon sizes after germination were transplanted into black plastic containers holding 10 L Hoagland solution with high B (25 μM H3BO3) or deficient B (0.25 μM H3BO3) levels. The nutrient solution was constantly aerated throughout the experiments and refreshed every 5 d, with one-quarter-strength solution initially added and increasing to one-half strength and eventually full strength. The seedlings were cultivated in an illuminated growth chamber (300–320 μmol m 2 s 1; 24 °C daytime: 22 °C night; 16 h photoperiod) for 20 d. After cultivation in the hydroponic culture, the roots of the seedlings were rinsed with running ultrapure-grade water to remove H3BO3 from the root surface. A microscopy analysis was used to characterize the root architecture of the fresh seedlings. The seedlings were divided into the roots and the shoots, exposed to 105 °C for 30 min to achieve cell death and then oven-dried to a constant weight at 65 °C, and finally evaluated for the shoot and root dry weight. To further study the genetic effects of qBEC-A3a on seed productivity, NILQ and NILW were subjected to yield assessments at the maturity stage by pot culture, with the B-efficient parent QY10 and B-inefficient parent W10 used as the controls. Each pot contained 7 kg grey purple sandy soil derived from sandy shale, and the basic agrochemical profiles were as follows: pH (1:1 H2O w/v) 7.7, organic matter 1.33 g kg 1, total nitrogen 0.25 g kg 1, total P 0.072 g kg 1 and hot water-soluble boron (HWSB) 0.10 mg kg 1. The low-B (0.25 mg B per kg soil) and high B (1.00 mg B per kg soil) treatments were performed, and each line, including QY10 and W10, included five replicates. The plants were irrigated with ultrapure water (18.25 MΩ· cm) at all of the growth stages. The mature plants were air-dried after harvest, and then the seed weight per plant was assessed. The ratio of the seed weight produced by each plant under low-B conditions relative to that under high-B conditions is defined as the B efficiency coefficient (BEC) (Zhang et al. 2014). To identify the B efficiency of the plants in the segregating population, the growing plants were observed to monitor for B-deficiency symptoms. If the plant showed obvious B-deficiency symptoms, including curved, thickened and dark young leaves and retarded root growth, it was assessed as B inefficient regardless of its biomass, and vice versa. The total dry weight of the plants was then subjected to a quantitative analysis. To identify the differential resistances of NILQ and NILW to low B at the cellular levels, suspension cells of these lines were cultivated in the B5 medium by using a slightly modified method that was previously established in B. napus (Yang 2012). The cells were subsequently analysed by in situ confocal laser-scanning microscopy, and the overall viability of the suspension cells with 10 replicates was quantified by IMAGEJ (National Institute of Mental Health, Bethesda, Maryland, USA) (http://rsb.info.nih.gov/ij/). In addition, pieces of the juvenile leaves (approximately 1 mm2) of the NILs under B-deficiency conditions were subjected to transmission electron microscopy (TEM) (H-7650; Hitachi, Tokyo, Japan) to characterize differences in the cell wall. © 2016 John Wiley & Sons Ltd, Plant, Cell and Environment, 39, 1601–1618

Boron accumulation quantification The dry samples, including the shoots and roots, were ground into a fine powder by using a carnelian mortar, and B was lixiviated from the dry powder by digestion in 1 M HCl in a shaker for 2 h and then assessed by inductively coupled plasma optical emission spectrometry (ICP-OES) by using an IRIS Advantage instrument (Thermo Electron, Waltham, Massachusetts, USA) in accordance with a previously described method (Zhang et al. 2014).

Molecular marker genotyping Whole-genome re-sequencing combined with BSA were performed to develop molecular markers that were tightly linked to the target QTL. A total of 20.0 Gb (approximately 17× depth) of high-quality sequences were generated for QY10, W10, B-efficient and B-inefficient DNA bulks by using a paired-end (PE; read length = 100 bp) sequencing strategy on an Illumina HiSeq 2000 system (San Diego, CA, USA), respectively. The InDel sites were detected by the quality-control (QC) alignment of reads and filtered according to B. napus ‘Darmor-bzh’ genome reference sequences (chromosome v1.0), and then primers were designed by using PRIMER PREMIER 5.0 (Premier Biosoft, CA, USA). The resulting polymorphic InDel-based molecular markers, which were codominant for the QY10 and W10 alleles, were used to genotype the segregating populations. Fresh young leaves from rapeseed seedlings were used for the total genomic DNA (gDNA) isolation according to the cetyltrimethylammonium bromide (CTAB) mini-prep method (Murray & Thompson 1980). The regimes used for the touchdown PCR assay were as follows: thermo-cycling at 94 °C for 4 min; followed by 10 cycles of 94 °C for 30 s, 60 °C for 30 s and 72 °C for 30 s, with the Tm descending 0.5 °C per cycle; 25 cycles of 94 °C for 30 s, 55 °C for 30 s and 72 °C for 30 s; and a final extension at 72 °C for 10 min, with the temperature then held at 22 °C. The PCR products were subjected to electrophoresis on a 6% (w/v) denaturing polyacrylamide gel, fractionated in 0.5× tris-borate-EDTA buffer (TBE) buffer and then subjected to silver staining for visualization.

Construction of genetic linkage maps and physical maps A local genetic linkage map was constructed by using JOINMAP 4.0 (Kyazma, Wageningen, Netherlands) (van Ooijen 2006), and the threshold logarithm of odds (LOD) value was set to 2.0. The Kosambi function was used to convert recombinant frequencies into map genetic distances in centimorgans (cM) (Kosambi 1944). According to the physical location of the molecular markers and genes retrieved from the Brassica database (BRAD) (http://brassicadb.org/brad/index.php) (Cheng et al. 2011) and CoGe (https://genomevolution.org/CoGe/CoGeBlast. pl), physical maps were generated by using MAPINSPECT (Wageningen UR Plant Breeding, Wageningen, Netherlands) (http://www.softsea.com/download/MapInspect.html).

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QTL-seq analysis A well-established QTL-seq approach, which is used for the rapid identification of plant QTLs by whole-genome re-sequencing of DNAs from two populations each composed of 20–50 individuals showing extreme opposite trait values for a given phenotype in a segregating progeny, relying on an evaluation of the SNP index and Δ(SNP index) (Takagi et al. 2013) was slightly modified to validate the candidate genomic region (s) harbouring the qBEC-A3a QTL. In the present study, we used a slightly modified QTL-seq method to perform the QTL analysis in B. napus, and the genome of ‘Darmor-bzh’ obtained by de novo sequencing (Chalhoub et al. 2014) was used as the reference to calculate the SNP index of the B-efficient (BE) and B-inefficient (B-inE) bulk rather than the sequence data of either of the two parents. The Δ(SNP index) was determined by subtracting the SNP index for the B-efficient (BE) bulk from the SNP index for the B-inefficient (B-inE) bulk.

Fine mapping of qBEC-A3a To further refine the position of the qBEC-A3a QTL, a recombinant-derived progeny testing strategy (Yang et al. 2012) was used to perform stepwise QTL fine mapping. Firstly, recombinants from the 1349 BC4F3 plants were selected according to the flanking InDel-based molecular markers that were newly developed within the QTL interval by WGS. In each recombinant family, the genotypes for all of the markers in the target region were determined. Then, the BC4F3:4 families derived from the BC4F3 recombinants were subjected to a hydroponic culture system for the B efficiency evaluation, which included B-deficiency symptoms and the total dry biomass. Similarly, the BC4F4 population was genotyped and the recombinant-derived BC4F4:5 families were then phenotyped to delimit the QTL region.

Characterization of differentially expressed genes The leaves and roots of QY10, W10 and NILQ seedlings grown under hydroponic culture for 10 d under low-B conditions (0.25 μM) were individually harvested, and each sample included three independently biological replicates for the analysis of DGE profiling. The total RNA of each sample was extracted by using pre-chilled TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s recommendations. DGE sequencing libraries were generated by using the NEBNext Ultra™ RNA library prep kit for Illumina (NEB, New England, North Dakota, USA) following the manufacturer’s recommendations, and they were subsequently sequenced on an Illumina HiSeq 2500 platform to generate 50 bp single-end (SE) reads. The high-quality clean reads were mapped to the transcriptome reference, and then the mRNA abundance of the unigenes identified by TOPHAT (CENTER FOR BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, UNIVERSITY OF MARYLAND, COLLEGE PARK, WASHINGTON D.C., USA) (http://ccb.jhu.edu/ software/tophat/index.shtml) and CUFFLINKS (Cole Trapnell’

lab, University of Washington, Seattle, Washington, USA) (http://cole-trapnell-lab.github.io/cufflinks/) (Trapnell et al. 2012) were normalized by the fragments per kilobase of exon model per million mapped reads (FPKM) (Trapnell et al. 2010). The DEGs were defined as those with a P value and false discovery rate (FDR) that were less than 0.05 (Secco et al. 2013). MultiExperiment Viewer (MEV) (DanaFarber Cancer Institute, Boston, Massachusetts, USA) (http://www.tm4.org/mev.html) (Eisen et al. 1998) was used to delineate heat maps based on the DGE results. A gene ontology (GO) analysis of the DEGs was performed by the PANTHER Classification System (http://www.pantherdb. org/data/) (Mi et al. 2005).In addition, gene co-expression networks were constructed to identify gene interactions and locate core genes that connect most neighbouring genes involved in the response to B deficiency in B. napus. For each pair of genes, the threshold of the Pearson correlation value was set according to the default settings (http://plantgrn.noble.org/DeGNServer/Analysis.jsp), and then gene coexpression networks were constructed by CYTOSCAPE 3.2.1 (Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada) (http://www. cytoscape.org/) (Kohl et al. 2011).

Validation of the digital gene expression results by real-time quantitative PCR Semi-quantitative PCR and real-time quantitative PCR (RTqPCR) assays were used to verify the DGE results. Total RNA was extracted and reversely transcribed as described earlier for the DGE library preparation. RT-qPCR was performed to detect the relative expression of genes by using the SYBR Green real-time PCR master mix kit (Toyobo, Osaka, Japan) and the CFX96TM real-time PCR detection system (Bio-Rad, Hercules, CA, USA). The PCR protocols were performed as follows: 95 °C for 1 min and then 45 cycles of 95 °C for 10 s, 58 °C for 15 s and 72 °C for 15 s. The expression data were normalized according to the expression level of actin by the 2 ΔΔCT method (Livak & Schmittgen 2001).

Molecular characteristics analysis of BnaA3.NIP5;1 To isolate the genomic sequence of BnaA3.NIP5;1, its fulllength coding sequence (CDS) was first amplified by PCR by using synthesized cDNA as the template and Darmor-bzh as the reference. Similarly, gDNA was used as the template to amplify UTRs. The cis-acting regulatory elements in the promoter were in silico analysed by using Plant Cis-acting Regulatory DNA Elements (PLACE) (Department of Genetic Resources, National Institute of Agrobiological Resources, Tsukuba, Japan) (http://www.dna.affrc.go.jp/htdocs/PLACE/) (Higo et al. 1999). The physical and chemical parameters of the BnaA3. NIP5;1 protein were calculated by PROTPARAM (Swiss Institute of Bioinformatics, Lausanne, Switzerland) (http://web. expasy.org/protparam/) (Gasteiger et al. 2005), and the hydrophobicity was plotted by a DNASTAR (Madison, Wisconsin, © 2016 John Wiley & Sons Ltd, Plant, Cell and Environment, 39, 1601–1618

Fine mapping a boron efficiency gene in rapeseed 1605 USA) LASERGENE PROTEAN analysis (http://www.dnastar. com/) (Burland 2000). Transmembrane helices of the BnaA3. NIP5;1 protein were predicted by using TMHMM Server v.2.0 (Health Sciences Library System, University of Pittsburgh, Pennsylvania, USA) (http://www.cbs.dtu.dk/services/TMHMM2.0/) (Krogh et al. 2001), and its three-dimensional (3D) structure was modelled by PHYRE2.0 (Structural Bioinformatics Group, Imperial College, London, UK) (http://www.sbg.bio.ic.ac.uk/ phyre2/html/page.cgi?id=index) (Kelley et al. 2015). The protein subcellular localization of BnaA3.NIP5;1 was predicted by WOLF PSORT (Computational Biology Research Center, AIST, Tokyo, Japan) (http://www.genscript.com/psort/ wolf_psort.html) (Horton et al. 2007). The conserved domains of the amino acid sequences of NIP5;1 were identified by using WEBLOGO (Department of Plant and Microbial Biology, University of California, Berkeley, USA) (http://weblogo. berkeley.edu/logo.cgi) (Crooks et al. 2004). For the phylogenetic and sequence alignment analyses, NIP-like gene sequences were retrieved from the GenBank (http://www.ncbi. nlm.nih.gov/) database, the Phytozome (www.phytozome.net) database, the Arabidopsis Information Resource (TAIR) (http://www.arabidopsis.org) database and BRAD (http:// brassicadb.org/brad/index.php) (Cheng et al. 2011). Multiple amino acid sequence alignments were performed by CLUSTALW (Bioinformatics Center, Kyoto University, Kyoto, Japan) (http://www.ebi.ac.uk/Tools/msa/clustalw2/) by using the default settings (Li et al. 2015). The phylogenetic tree for the putative relatives of NIP5;1 was constructed by using the neighbour-joining (NJ) algorithm in Molecular Evolutionary Genetics Analysis version 6.0 (MEGA6.0) (Research Center for Genomics and Bioinformatics and Department of Biological Sciences, Tokyo Metropolitan University, Hachioji, Tokyo, Japan) (http://www.megasoftware.net/) (Tamura et al. 2013). The distributions of conserved motifs in BnaA3.NIP5;1 and other NIP family members were identified by using Multiple Em for Motif Elicitation (MEME) suite 4.10.1 (Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia; Department of Genome Sciences, University of Washington, Seattle, Washington, USA) (http:// meme-suite.org/tools/meme) (Bailey et al. 2009) using the complete amino acid sequences.

Statistical analysis The goodness of fit of the observed phenotypes and expected segregation ratio was determined by using chi-squared tests (χ 2) (Ryabko et al. 2004). Fisher’s least significant difference (LSD) test (P value < 0.05) was performed for all statistical tests analyses by using the software Statistical Product and Service Solutions 17.0 (SPSS, Chicago, IL, USA).

RESULTS Genetic analysis of qBEC-A3a under B deficiency A randomly selected BC4F2 population that was generated from a heterozygous NIL (ZA-19) carrying qBEC-A3a (Supporting Information Fig. S1a) (Zhang et al. 2014) was © 2016 John Wiley & Sons Ltd, Plant, Cell and Environment, 39, 1601–1618

subjected to a B efficiency segregation analysis to assess the dry weight and B-deficiency symptoms under B-limitation conditions (0.25 μM) by using a hydroponic culture system. The segregating population presented a 3:1 segregation ratio of B-efficient to B-inefficient plants, which was consistent with the expected segregation (χ 2 = 0.53 < χ 20.05 = 3.84) (Supporting Information Table S1). The results indicate that the regulation of B efficiency by qBEC-A3a in B. napus behaved as a single Mendelian factor. Moreover, the dry weight of the segregating BC4F2 population ranged from 0.037 to 0.67 g per plant (Supporting Information Fig. S1b). The dry weight distribution in the W10 homozygous (NILW), heterozygous (NILQ/W) and QY10 homozygous (NILQ) alleles was 0.012–0.27 g per plant (mean of 0.089 g per plant), 0.0088–0.66 g per plant (mean of 0.21 g per plant) and 0.037–0.67 g per plant (mean of 0.32 g per plant), respectively (Supporting Information Fig. S1c). These results demonstrate that the regulation of B efficiency by qBEC-A3a was inherited in a partially dominant manner.

Genetic effects of qBEC-A3a on B efficiency To assess the effect of qBEC-A3a on resistance to B deficiency, that is B efficiency, the NILQ and NILW plants were exposed to B-deficient conditions to determine their phenotypes at the seedling and maturity stages. Under hydroponic culture, the NILQ plants did not exhibit any evidence of B-deficiency symptoms, including retarded growth, curved leaves and increased lateral roots, and the plants outperformed both the W10 and NILW plants (Fig. 1a–c). In terms of the root systems, the B-efficient genotypes (QY10 and NILQ) developed longer total root lengths, larger root surface areas and root volumes compared with the B-inefficient genotypes (W10 and NILW) (Supporting Information Fig. S2). A stereomicroscopy analysis showed that the density and length of the root hairs (Fig. 1d) in the root hair zone of the NILQ plants were much fewer than those of the NILW plants (Fig. 1e), and a TEM analysis showed that the cell wall of the B-inefficient NILW plants (Fig. 1g,i) was much thicker than that of the B-efficient NILQ plants (Fig. 1f,h), which was a result of the plants’ greater sensitivity to B deficiency. Moreover, under B-limitation conditions (0.1 μM), the viability of NILQ suspension cells (Fig. 1l,n, Supporting Information Fig. S3c) was much higher than that of NILW suspension cells, which also showed abnormal morphologies (Fig. 1m,n, Supporting Information Fig. S3d), although the cells were similar under the high-B conditions (50 μM) (Fig. 1j,k,n, Supporting Information Fig. S3a,b). In addition, under both the high- and low-B conditions, the dry weights of the shoots and roots of the NILQ plants were similar to those of the B-efficient parent QY10, which were markedly higher than those of the NILW and W10 plants (Fig. 2a,b). The accumulation of B in the shoots was much higher than that in the roots for these four lines. Surprisingly, the accumulation of B in the shoots and the roots of the NILQ line was considerably higher than those of NILW and W10 lines under B-deficiency conditions (Fig. 2c,d), which indicates that the B-efficient lines carrying qBECA3a accumulated more B relative to the B-inefficient ones.

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Figure 1. Performance of QY10, W10, NILQ and NILW. (a) Growth performance of B-efficient QY10, B-inefficient W10, B-efficient NILQ and W

Q

W

NIL grown under low B (0.25 μM) for 20 d under hydroponic culture (scale bar = 5 cm); (b,c) interior leaf morphology of NIL (b) and NIL (c) Q W (scale bar = 3 mm); (d,e) root hair morphology (scale bar = 500 μm) of NIL (d) and NIL (e); zoomed-out (f,g) and close-up (h,i) views of cell walls Q W Q W (CW) in NIL and NIL cultivated under B deficiency (0.25 μM); (j–n) viability of suspension cells of NIL (j) and NIL (k) under high B (50 μM) Q W and NIL (l) and NIL (m) under low B (0.1 μM) [cells were cultivated for 8 d in the B5 medium and analysed by in situ confocal laser-scanning microscopy, which indicated that the suspension cells remained viable (green) or died (red) (scale bar = 30 μm)]; (n) overall viability of suspension cells Q W from NIL and NIL . Bars denote the means (n = 10), and error bars denote the standard deviation (SD). The significance level was set at a P value < 0.05. Q W NIL and NIL represent near-isogenic lines (NILs) carrying the homozygous alleles from QY10 and W10 in the QTL region harbouring qBECA3a, respectively.

To determine whether qBEC-A3a could be used to enhance seed yields, the performance of mature NILs was further evaluated under pot culture. Under B limitation conditions, there were more null pods (Fig. 3a) and shrivelled seeds (Fig. 3b) in NILW, which presented a lower seed yield compared with NILQ (Fig. 3c). Furthermore, the BEC of NILQ was similar to that of QY10 but higher than that of W10 and NILW (Fig. 3d).

Validation of the target quantitative trait locus qBEC-A3a location by QTL-seq To precisely map or even positionally clone the major QTL qBEC-A3a, which confers tolerance to B deficiencies, a strategy that combines WGS with BSA was employed to develop molecular markers, including InDel and SNPs that were tightly linked to the target QTL. According to a well-established QTL-seq approach (Takagi et al. 2013) and SNP sites generated from the BSA and WGS, SNP-index plots of the B-efficient bulk (BE-bulk) and B-inefficient bulk (BinE-bulk) were delineated followed by the depiction of Δ(SNP index), respectively (Fig. 4). The result indicates that a single peak occurred in the genomic region (9 746 809–12 211 917 bp) of chromosome A3, which harbours a candidate QTL. This result

demonstrates the unique presence of the QTL for B efficiency on chromosome A3, which has the potential for use in the fine mapping of qBEC-A3a.

Delimitation of a candidate genomic region harbouring qBEC-A3a To further refine the position of qBEC-A3a, a stepwise fine mapping strategy based on the recombinant-derived progeny testing was used to perform QTL fine mapping. Firstly, a BC4F3 population consisting of 1349 plants was screened, and the recombinants containing crossover events between the flanking InDel-based markers A03-06 and A03-76 (Fig. 5a) were identified. These recombinants were then genotyped with 15 InDel-based molecular markers developed from the WGS (Supporting Information Table S2). Subsequently, a local genetic linkage map covering 6.6 cM was constructed (Fig. 5b, Supporting Information Fig. S4a), and it was consistent with the physical map (Supporting Information Fig. S4a, b). Based on their genotypes, the BC4F3 recombinants were categorized into 10 groups (Fig. 5c). Then, the BC4F3:4 families derived from the BC4F3 recombinants were assessed in a B efficiency evaluation by using a hydroponic culture system, with QY10 and W10 included as the controls. Group A1 contained two © 2016 John Wiley & Sons Ltd, Plant, Cell and Environment, 39, 1601–1618

Fine mapping a boron efficiency gene in rapeseed 1607

Figure 2. Dry weight and B accumulation of QY10, W10, NILQ and NILW grown under low-B and high-B conditions. Dry weight of the shoot (a) Q

W

and the root (b) and B accumulation of the shoot (c) and the root (d) of QY10, W10, NIL and NIL grown under low-B (LB, 0.25 μM) and high-B (HB; 25 μM) conditions for 20 d in a hydroponic culture. The significance level was set at a P value < 0.05. Bars denote the means (n = 3), and error bars denote the standard deviation (SD).

Figure 3. Performance of QY10, W10, NILQ and NILW cultivated by pot culture at the maturity stage. (a) Growth performance of NILQ and NILW under low-B conditions (0.25 mg B per kg soil). Null pods are indicated by red arrows. Scale bar = 20 cm. (b) One hundred seeds randomly selected Q W Q W from NIL (left) and NIL (right) under B deficiency. Scale bar = 3 cm. (c) Seed weight produced by QY10, W10, NIL and NIL . HB and LB 1 1 denote high B (1.0 mg kg ) and low B (0.25 mg kg ), respectively. Statistical analyses were performed separately for the data obtained from the highB and low-B treatments. The significance level was set at a P value < 0.05. Bars denote the means (n = 3), and error bars denote the standard deviation Q W (SD). (d) B efficiency coefficient (BEC) of QY10, W10, NIL and NIL . BEC is defined as the ratio of the seed weight produced by each plant under low-B conditions relative to that under high-B conditions (Zhang et al. 2014).

recombinants between the markers BnGMS436 and A03-76, and they were B efficient relative to the B-inefficient W10. In addition, the two recombinants in the contrasting group A2 © 2016 John Wiley & Sons Ltd, Plant, Cell and Environment, 39, 1601–1618

showed typical B-deficiency symptoms with small biomasses and were considered to be B inefficient. Thus, group A1 and A2 confined qBEC-A3a to a genomic region between the

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Figure 4. SNP-index graphs depicting the BE (B-efficient) bulk, BinE (B-inefficient) bulk and Δ(SNP index) graphs generated from the QTL-seq analysis. The X-axis denotes the physical positions (Mb) on chromosome A3 in Brassica napus. The Y-axis represents the SNP index, which was estimated according to a 5 Mb physical interval with a 10 kb sliding window (Takagi et al. 2013). Each red dot represents an SNP site along chromosome A3, and the Δ(SNP index) was plotted by subtracting the BE bulk from the BinE bulk. A candidate major genomic interval (marked by an asterisk) harbouring the B efficiency QTL qBEC-A3b was defined by using the criteria of the SNP index (Takagi et al. 2013).

markers BnGMS436 and A03-76, and qBEC-A3a was further delimited to a 1.7 cM (0.9 Mb) interval between the markers A03-52 and A03-76 by substitution mapping (Fig. 5c). Similarly, the 2049 BC4F4 plants were then screened, and the recombinants containing crossover events between the flanking markers A03-52 and A03-76 were identified. Eventually, the QTL region harbouring qBEC-A3a was further narrowed down to a smaller genomic interval spanning 119 kb between the markers A03-66 and A03-71 (Fig. 5d,e), which co-segregated with qBEC-A3a. Within this region, 21 open reading frames (ORFs) were annotated (Supporting Information Table S3) according to the available B. napus genome sequence database, and they were greatly collinear to the corresponding genes on chromosome 4 of the P block in Arabidopsis (Fig. 5f). Noticeably, among the annotated genes within the QTL region, ORF16 (BnaA03g24370D) was homologous to AtNIP5;1 (AT4G10380), an established influx boric acid channel in Arabidopsis essential for efficient B uptake and plant development under low-B conditions (Takano et al. 2006), which implied that BnaA03g24370D may be a candidate gene underlying qBEC-A3a prior to the other annotated genes.

Identification of candidate genes through integrating quantitative trait locus fine mapping with digital gene expression profiling To accelerate the identification of candidate genes underlying qBEC-A3a in B. napus, DGE profiling of the B-efficient

QY10, B-inefficient W10 and B-efficient NILQ under Bdeficiency conditions was performed to characterize the DEGs within the QTL interval defined by fine mapping. The summary of the mean data generated by DGE is shown in Supporting Information Table S4. A grafting experiment in a previous study demonstrated that B efficiency in B. napus is primarily controlled by the roots, thus allowing the uptake and accumulation of greater amounts of B in B-efficient cultivars relative to B-inefficient cultivars under B limitation conditions (Yang et al. 2013). Hence, the candidate genes underlying qBEC-A3a were concentrated on the DEGs that had uniquely significantly higher expression levels in the roots of QY10 and NILQ than those in the roots of W10. An analysis of the DEGs within the 119 kb QTL region between QY10 and W10 and NILQ combined with W10 showed that the expression level of ORF16 (BnaA03g24370D) in the roots of both QY10 and NILQ was uniquely markedly higher than in the roots of W10 (Fig. 6a,b). The BnaA03g24370D homolog from Arabidopsis was detected in the National Center for Biotechnology Information (NCBI) database and BRAD, and it was annotated as AtNIP5;1 (AT4G10380), which is an influx boric acid channel essential for B-efficient uptake and plant normal development under B-limitation conditions (Takano et al. 2006). Therefore, the BnaA03g24370D gene, which was considered as the candidate gene underlying qBEC-A3a, was tentatively referred to as BnaA3.NIP5;1. Subsequently, semi-quantitative PCR and RT-qPCR assays were performed to further validate the expression difference of © 2016 John Wiley & Sons Ltd, Plant, Cell and Environment, 39, 1601–1618

Fine mapping a boron efficiency gene in rapeseed 1609

Figure 5. Fine mapping of qBEC-A3a by a two-step substitution mapping strategy. (a) Physical distance corresponding to the QTL confidence interval defined by both the QTL-seq and the flanking markers A03-06 and A03-76. (b) Local genetic linkage map of the qBEC-A3a region on chromosome A3 based on genotyping 1349 BC4F2 plants. Numbers above the chromosome indicate the genetic distance between adjacent markers. (c) Progeny testing of BC4F3/BC4F3:4 plants delimiting the qBEC-A3a locus to the region between markers A03-52 and A03-76. (d) Physical mapping of the genomic region harbouring qBEC-A3a between the markers A03-52 and A03-76. (e) Progeny testing of BC4F4/BC4F4:5 plants delimiting the qBEC-A3a locus to the region between the markers A03-66 and A03-71. (f) Comparative mapping of the target QTL region in Brassicaceae species, including Arabidopsis thaliana (A. thaliana), Arabidopsis lyrata (A. lyrata), Capsella rubella (C. rubella), Schrenkiella parvula (S. parvula), Brassica rapa (B. rapa), Brassica oleracea (B. oleracea), Brassica napus (B. napus) and 21 potential open reading frames (ORFs) within the QTL interval spanning a 119 kb genomic region according to the annotated B. napus genome sequence. Homologs between Arabidopsis and B. napus within the QTL interval are denoted by dotted lines, and the corresponding genomic region belonging to the P block in Arabidopsis harbours an established B influx transporter gene NIP5;1 (Takano et al. 2006), which is indicated by a red star. Each ORF is indicated by an arrow. © 2016 John Wiley & Sons Ltd, Plant, Cell and Environment, 39, 1601–1618

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Figure 6. Candidate genes analysis based on QTL fine mapping and digital gene expression (DGE) profiles. (a) Expression profiling of 21 genes annotated within the QTL region from the DGE results of the shoot (S) and the root (R) among Q, W and N. Q, W and N represents QY10, W10 and Q Q NIL , respectively. Differentially expressed genes (DEGs) between QY10 and W10 or NIL and W10 are indicated by asterisks. (b) Venn diagram Q showing that the DEGs that were significantly higher in QY10 or NIL than W10 within the 119 kb QTL interval harbouring qBEC-A3a in the shoot Q (S) and the root (R) were overlapped among QY10, W10 and NIL under low B. The unique candidate gene ORF16 (BnaA3.NIP5;1) within the QTL region is denoted by a star. (c) Validation of BnaA3.NIP5;1 mRNA abundance in QY10 and W10 under long-term B deficiency (0.25 μM) by semiquantitative RT-PCR. (d-e) Validation of BnaA3.NIP5;1 expression level of the root under B deficiency by qRT-PCR. (d) Brassica napus seedlings cultivated in a hydroponic culture under low B (LB; 0.25 μM) conditions for 10 d. (e) Brassica napus seedlings cultivated in a hydroponic culture under high-B (HB, 10 μM) conditions for 10 d and then transferred to the solution without B; B resupply was performed after 5 d of B-free treatment.

BnaA3.NIP5;1 between QY10 and W10. The semiquantitative PCR results showed that BnaA3.NIP5;1 was preferentially expressed in the root compared with the shoot irrespective of high-B or low-B conditions (Fig. 6c), which is equivalent to the results for AtNIP5;1 (Takano et al. 2006). In addition, the expression level of BnaA3.NIP5;1 in the roots of QY10 was strikingly higher than that in the roots of W10 (Fig. 6c–e), which is consistent with the DGE results (Supporting Information Fig. S5). Moreover, the abundance of BnaA3.NIP5;1 mRNA under short-term B-deficiency conditions was suppressed after B had been resupplied (Fig. 6d), indicating that BnaA3.NIP5;1 acted in a B-dependent manner similar to AtNIP5;1 (Takano et al. 2006). Taken together, these results show that BnaA3.NIP5;1 is the prior candidate gene underlying the major-effect QTL qBEC-A3a, which is principally responsible for regulating B efficiency in B. napus.

Molecular characteristics of BnaA3.NIP5;1 Genomic and cDNA sequence analyses (GenBank accession nos. KT899998, KT899999) revealed that BnaA3.NIP5;1 contained a complete ORF of 906 bp, which included four

exons and three introns, and was flanked by a 463-bp 5′ UTR and a 315-bp 3′ UTR (Fig. 7a). Although two nucleotide differences occurred in the second exons between QY10 and W10 (Fig. 7b), the variations in nucleotides did not lead to amino acid changes (CTA790/Leu to CTT790/Leu, ATC1066/Ile to ATA1066/Ile) (Figs 7b & 8a). A sequence analysis of BnaA3. NIP5;1 in the B-efficient QY10 and B-inefficient W10 revealed an InDel polymorphism at the position +6 (D1: /TTTCT) and 11 SNPs at the positions +13 (S1: T/C), +60 (S2: T/G), +148 (S3: T/C), +214 (S4: C/A), +233 (S5: C/G), +328 (S6: T/C), +348 (S7: T/A), +403 (S8: C/A), +410 (S9: A/C), +411 (S10: C/A) and +416 (S11: T/A) in the 5′ UTR and 6 SNPs at the positions +1404 (S1: C/T), +1530 (S2: T/A), +1561 (S3: T/C), +1566 (S4: C/T), +1611 (S5: G/A) and +1641 (S6: A/G) in the 3′ UTR (Fig. 7b). Moreover, another two B. napus cultivars, B-efficient Zhongshuang 11 (ZS11) and B-inefficient Bakow (Xu et al. 2001) (Fig. 7c), presented allelic polymorphisms in the 5′ UTR (GenBank accession nos. KT900000, KT900001) of BnaA3.NIP5;1 that remained completely conserved (Fig. 7c). In addition, this region presented two highly conserved sequences (sequence 1: +188AGCAUGUAAAUU+200; sequence 2: +358UCAAAUCAUGUAAAUUU+374) (Fig. 7d) © 2016 John Wiley & Sons Ltd, Plant, Cell and Environment, 39, 1601–1618

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Figure 7. Comparison of the genomic sequences of BnaA3.NIP5;1 between QY10 and W10. (a) BnaA3.NIP5;1 is composed of four exons and three introns. Solid green and red boxes indicate the coding sequences CDs) and untranslated regions (UTRs), respectively, and lines represent introns. The position of the putative transcription start site (TSS) in the cDNA is defined by ‘+1’. The upstream open reading frames (uORFs) within the 5′ UTR are marked by arrows. (b) The nucleotide differences of BnaA3.NIP5;1 between QY10 and W10. (c) Phenotypic performance of B-efficient Zhongshuang11 (ZS11) and B-inefficient Bakow, which were cultivated in a hydroponic culture under B-limitation conditions (0.25 μM) for 10 d. Scale bar = 5 cm. (d) Comparison of the sequences of the 5′ UTR of Arabidopsis NIP5;1 and BnaA3.NIP5;1 of QY10, W10 and Darmor-bzh. Conserved regions among the sequences are listed, and the short uORFs (AUG) that regulate B-dependent expression (Tanaka et al. 2011) are underlined. (e) Conservative sequence analysis of the 5′ UTR of NIP5;1 and NIP5;1-like genes in diverse plant species, which include Arabidopsis thaliana (AtNIP5;1), Brassica napus (BnaA3.NIP5;1), Citrus sinensis (CiNIP5), Vitis vinifera (VvNIP3;1), Brachypodium distachyon (BradiNIP3;1), Sorghum bicolor (SbNIP3;1), Oryza sativa (OsNIP3;1) and Zea mays (ZmNIP3;1). The short uORFs (AUG) are boxed.

compared with AtNIP5;1, and these sequences were identified necessary for the regulation of B-dependent expression (Tanaka et al. 2011). Moreover, a sequence analysis of the 5′ UTR in diverse plant species, which include Arabidopsis thaliana, B. napus, Citrus sinensis, Vitis vinifera, Brachypodium distachyon, Sorghum bicolor, Oryza sativa and Zea mays, further revealed two conserved sequences (sequence 1: CAUGUA; sequence 2: UCAAAUCAUGUAA) (Fig. 7e). A genome-wide BLASTn analysis of BRAD showed that BnaA03g24370D (BnaA3.NIP5;1) has five other homologs in B. napus (Supporting Information Fig. S6), and a blast search analysis against the GenBank Conserved Domain Database v3.12 (www.ncbi.nlm.nih.gov/cdd/) indicated that the BnaA3. NIP5;1 protein is a member of the major intrinsic protein (MIP) superfamily and shares a high identity (92.43%) with AtNIP5;1, which belongs to the NIP subfamily in the MIP superfamily. These results indicate that BnaA3.NIP5;1 contains two highly conserved Asn-Pro-Ala (NPA) motifs (NPA1: N137-P138-S139, NPA2: N248-P249-V250) that are identical to those of AtNIP5;1 and four amino acids residues (A197, I236, A245, R251) for the aromatic/arginine (ar/R) selectivity filter regions that are identical to those of AtNIP6;1 but different from those of AtNIP5;1 (Fig. 8a,b). Compared with AtNIP5;1, the three amino acids ‘G23-T24-P25’ in the mitogen-activated protein kinase (MPK) phosphorylation © 2016 John Wiley & Sons Ltd, Plant, Cell and Environment, 39, 1601–1618

site of BnaA3.NIP5;1 were missing. Gene structure and phylogenetic analyses of the NIP5;1 homologs in Brassicaceae species showed that BnaA3.NIP5;1 (BnaA03g24370D) in B. napus originated from BraA3.NIP5;1 (Bra000710) in B. rapa (Fig. 8c,d), and the micro-collinearity analysis of the genes flanked by BnaA3.NIP5;1 in Brassicaceae species (Supporting Information Fig. S7) indicated that the QTL region maintained a highly conserved genomic architecture during the evolution of allotetraploid rapeseed. In addition, predictions of subcellular localization showed that BnaA3. NIP5;1 should be located in the PM, which is identical to the localization in AtNIP5;1 and facilitates the influx of B into the root cells (Takano et al. 2006). BnaA3.NIP5;1 is a putative 301-residue polypeptide of ~31 kDa (pI: 8.90), and it was predicted to possess strong hydrophilicity (excluding the N- and C-terminus) (Fig. 9a), six transmembrane helices (helices 1–6) connected by two intracellular and three extracellular loops, and cytoplasmic N- and C-terminal extensions (Fig. 9b,c), and this protein structure is identical to that of previously reported NIPs (Rouge & Barre 2008). To verify the conserved regions among BnaA3.NIP5;1 and other NIP family members, a motif analysis was conducted with the MEME web server. Detailed sequence logos of the 20 motifs are shown in Supporting Information Table S5. Apparently, motifs 1, 2,

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Figure 8. Analysis of the predicted amino acid sequences of NIP5;1 in Brassicaceae species. (a) Mitogen-activated protein kinase (MPK) phosphorylation sites, with the NPA motifs (NPA1 and NPA2) denoted by red dotted boxes and ar/R selectivity filters (H2, H5, LE1, and LE2) indicated by red stars. (b) Conservative analysis of the amino acid sequences of NIP5;1 in Brassicaceae species, including Brassica napus, Brassica rapa and Brassica oleracea. The font size represents the number of conserved amino acid residues, with larger fonts indicating more conserved amino acid residues. (c) Comparative analysis of the gene structure of AtNIP5;1 (Arabidopsis), BraA3.NIP5;1 (B. rapa) and BnaA3.NIP5;1 (B. napus). Black boxes and lines represent exons and introns, respectively. (d) Phylogenetic analysis of NIP5;1 homologs in Brassicaceae species. AtNIP5;1 is indicated by a solid circle, and BnaA3.NIP5;1 is denoted by a red star.

3, 4, 6 and 11, which correspond to six transmembrane domains of NIPs, were highly conserved, and the NIP5;1 family members of different species, including BnaA3. NIP5;1, shared a considerable identity among their amino acid sequences (Fig. 9d). To further determine the phylogenetic relationship between BnaA3.NIP5;1 and other NIPs, a phylogenetic tree was constructed by using the NJ algorithm of MEGA6 (Fig. 9e), and the results show that the BnaA3.NIP5;1 protein fell into NIP subgroup II, which includes AtNIP5;1, OsNIP3;1, ZmNIP3;1 etc.

Boron transporters in response to B-deficiency exposures in B. napus The DGE profiling showed that NILQ had a greater number of DEGs in the roots compared with W10 (Supporting Information Fig. S8a), and the GO analysis indicated a considerable number of these DEGs were transporter genes (Supporting

Information Fig. S8b). Among the genome-wide transporters that respond to B deficiency in B. napus, the expression profiles of BnaNIP5;1s, BnaBOR1s and BnaNIP6;1s, which are homologous to AtNIP5;1, AtBOR1 and AtNIP6;1 in B. napus, respectively, were selected for analysis and characterization. In addition to BnaA3.NIP5;1, the mRNA abundance of BnaA4. BOR1 and BnaA2.NIP6;1 in NILQ was also significantly higher compared with that of W10 (Fig. 10a); thus, BnaA3.NIP5;1, BnaA4.BOR1 and BnaA2.NIP6;1 likely acted synergistically to promote B-efficient absorption, transport and distribution in the B-efficient lines. To further determine the roles of each putative B transporter gene (Fig. 10b) in response to B deficiencies in B. napus, a gene co-expression network was established based on the DGE profiles of BnaNIP5;1s, BnaBOR1s and BnaNIP6;1s. Evidently, among the genes correlated with B uptake and distribution in B. napus, BnaA3. NIP5;1, the candidate gene underlying the major-effect QTL qBEC-A3a for B efficiency, was located in the core region (Fig. 10c), which provides further evidence that BnaA3. © 2016 John Wiley & Sons Ltd, Plant, Cell and Environment, 39, 1601–1618

Fine mapping a boron efficiency gene in rapeseed 1613

Figure 9. Protein structure, conserved domains and phylogenetic analysis of BnaA3.NIP5;1. (a) Kyte–Doolittle (Kyte & Doolittle 1982) hydrophobicity plot of BnaA3.NIP5;1 using the DNASTAR LASERGENE PROTEAN (Burland 2000). Hydrophobicity and hydrophilicity of the amino acids were defined by positive and negative values. (b) Predicted topology of BnaA3.NIP5;1 in the membrane using TMHMM Server v.2.0 (Krogh et al. 2001). The numbers indicate the location of amino acids along the polypeptide of BnaA3.NIP5;1. (c) Three-dimensional (3D) protein structure of BnaA3.NIP5;1 as modelled by PHYRE2 (Kelley et al. 2015). (d) Distribution of conserved motifs in BnaA3.NIP5;1 and other NIP family members. All of the motifs were identified by MEME ((Bailey et al. 2009) by using the complete amino acid sequences. Different motifs are shown by different colours numbered 1–20. Distributions of six predicted transmembrane helices (helices 1–6) and two additional half-helices (loop-B and loop-E) are indicated at the bottom of the figure. (e) Phylogenetic analysis of the deduced amino acids of NIPs from various plant species, inferred by using the neighbourjoining (NJ) method. Bootstrap values were estimated (with 1000 replicates) to assess the relative support for each branch. Bootstrap values of 50% and above are indicated on the tree. BnaA3.NIP5;1 are indicated by a star.

NIP5;1 indeed plays a dominant role in the efficiency of B uptake in the roots of B. napus under B limitation conditions.

DISCUSSION Utility of QTL-seq analyses for the validation of major quantitative trait loci for B efficiency in B. napus In this study, QTL-seq ,which was used as an approach of the rapid identification of plant QTLs by whole-genome re-sequencing of DNAs from two populations each composed of 20–50 individuals showing extreme opposite trait values for a given phenotype in a segregating progeny, (Takagi et al. 2013) was first employed to validate the major-effect QTL qBEC-A3a for B efficiency in B. napus. This method incorporates high-throughput WGS along with BSA to genotype genome-wide SNPs of the B-efficient QY10 with the B-efficient progeny DNA pool and genotype the B-inefficient W10 with the B-inefficient progeny DNA pool. Subsequently, the SNP © 2016 John Wiley & Sons Ltd, Plant, Cell and Environment, 39, 1601–1618

index was used to perform accurate quantitative assessment of the frequencies of parental alleles as well as the genomic contribution from the two parents to BC4F2 individuals. These characteristics of QTL-seq make it a more rapid and efficient method of identifying or verifying genomic regions that harbours major QTLs for a target gene. Recently, QTL-seq has been demonstrated to be a cost-effective and efficient method for QTL identification in crop species with a compact genome, such as rice (Takagi et al. 2013), cucumber (Lu et al. 2014), chickpea (Das et al. 2015) and tomato (Illa-Berenguer et al. 2015). Nonetheless, few studies have identified and characterized QTLs by using the QTL-seq method in polyploid species with complex genomes, such as allotetraploid rapeseed (B. napus L.). In the present study, however, QTL-seq was successfully applied to validate the QTL for B efficiency in B. napus (AnAnCnCn, 2n = 4x = 38), a crop that has a genome with a size of up to 1130 Mb (Chalhoub et al. 2014). Additionally, the QTL interval characterized by QTL-seq was identical to the region defined by the InDel-based molecular markers (Figs 4 & 5a), thus demonstrating the availability and

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Figure 10. Overview of putative B transporters in Brassica napus. (a) Digital gene expression (DGE) profiles of putative B transporters in the shoot Q Q (S) and the root (R) of the B-efficient NIL (N) and B-inefficient W10 (W). The genes with expression levels of NIL that are significantly higher than those of W10 are denoted by asterisks. The significance level was set at a P value < 0.05. (b) A schematic diagram of the uptake, transport and distribution of B in Arabidopsis (Takano et al. 2002; Takano et al. 2006; Tanaka et al. 2008). (c) Co-expression network of BnaNIP5;1s, BnaBOR1s and BnaNIP6;1s in B. napus. The candidate gene BnaA3.NIP5;1 is denoted by a star. Cycle nodes represent genes, and the size of the nodes represents the power of the interrelation among the nodes by degree value. Edges between two nodes represent interactions between genes.

accuracy of QTL-seq in QTL analyses in B. napus. Therefore, the results indicate that QTL-seq can also be applied to rapidly and efficiently identify and delineate QTLs for traits of great interest in plant species with complex genomes, including B. napus.

Fine mapping of qBEC-A3a In the analyses of suspension cells and the plants at the seedling and maturity stages, the NILW line that does not carry qBECA3a was considerably more vulnerable to B deficiency relative to the B-efficient NILQ line (Figs 1 & 3). In addition, this phenotypic difference was well correlated with the differences in B accumulation and seed yield among the QTL NILs at the seedling and maturity stages (Figs 2 & 3). In conclusion, a single qBEC-A3a allele from the B-efficient QY10 line acts as a key determining factor in B efficiency and could be used to genetically improve the tolerance of B. napus with a W10 background

to B deficiency and considerably improve the total biomass and seed yield under B-limited conditions. Therefore, the Befficient NILs can ultimately contribute to the development of B-efficient oilseed rape varieties by breeders in the agricultural industry. Exploiting DNA sequence variations within a genome and mining molecular markers tightly linked to target QTLs is of the utmost value for mapping QTLs and genetic map-based cloning (Huang et al. 2009; Jeong et al. 2013; Lee et al. 2015). In our research, WGS was combined with BSA to successfully identify dozens of molecular markers (Supporting Information Table S2) that were closely linked to the target QTL qBECA3a, especially the InDel-based marker ID-NIP5;1. This marker was developed from the candidate gene BnaA3. NIP5;1, which co-segregates with qBEC-A3a. These findings can be used to integrate favourable qBEC-A3a alleles into Binefficient oilseed rape cultivars through marker-assisted selection (MAS) and further promote breeding programmes for B-efficient rapeseed germ plasm in the agricultural industry. © 2016 John Wiley & Sons Ltd, Plant, Cell and Environment, 39, 1601–1618

Fine mapping a boron efficiency gene in rapeseed 1615 Quantitative trait locus fine-scale mapping is a powerful tool and an indispensable prerequisite for thoroughly deciphering the genetic bases and molecular mechanisms underlying complicated agricultural traits, the majority of which are governed by multiple genes (Li et al. 2011), including B efficiency in B. napus (Zhang et al. 2014). Prior to this research, reports on the map-based cloning of genes that regulate B efficiency in B. napus were unavailable. In this study, two large segregating populations (BC4F3 and BC4F4) based on the NILs developed in B. napus (Zhang et al. 2014) were used in fine mapping the major B efficiency QTL qBEC-A3a according to the recombinant-derived progeny testing strategy, which is an efficient and powerful method of finely mapping QTLs within backcrossing populations. Based on sequential substitution mapping, the qBEC-A3a QTL was eventually narrowed down to a genomic interval spanning 119 kb harbouring 21 annotated genes (Fig. 5), and this finding lays a solid foundation for revealing candidate genes that regulate B efficiency in B. napus.

Feasibility of combining quantitative gene locus fine mapping with digital gene expression analysis Digital gene expression profiling is a revolutionary approach for gene expression analyses (Asmann et al. 2009; Hao et al. 2011). Driven by Illumina sequencing technology, DGE profiling creates genome-wide expression profiles under Blimited conditions by sequencing the B-efficient and the Binefficient lines, which is indispensable for understanding the transcriptional changes underlying the differential responses to B deficiency in B. napus. In addition, the extensive collinearity of the genomes between Arabidopsis and B. napus (https://genomevolution.org/CoGe/SynMap.pl) (Supporting Information Fig. S9) provides a considerable reference for the analysis of candidate genes within the QTL interval (Fig. 5f). In this research, QTL fine mapping, comparative genomics and DEG analyses were combined for the first time, and this approach successfully reduced the number of candidate genes regulating B efficiency within the QTL confidence interval within a shorter time and delimited 21 ORFs to only 1 prior candidate gene that underlies the target QTL (Fig. 6a,b). Accordingly, integrating QTL mapping and comparative genomics with a transcriptomics analysis of DEGs can provide novel insights into the rapid cloning quantitative trait genes (QTGs) in crop species with complex genomes owing to the cost-effectiveness and high efficiency of this approach.

Potential regulation of BnaA3.NIP5;1 as a nodulin 26-like intrinsic protein family member Phylogenetic analyses revealed that the candidate BnaA3. NIP5;1 underlying qBEC-A3a is a member of NIP subgroup II (Fig. 9e). The NIP family is a group of highly conserved multifunctional MIPs specific to plants (Wallace et al. 2006). All NIPs possess ar/R regions and NPA motifs that exhibit in a typical α-helical fold into the core of AQPs to © 2016 John Wiley & Sons Ltd, Plant, Cell and Environment, 39, 1601–1618

form two major constriction filters along the central water channel (Mitani-Ueno et al. 2011). Without exception, the amino acid sequence analyses of BnaA3.NIP5;1 revealed highly conserved NPA motifs (NPA1: N137-P138-S139; NPA2: N248-P249-V250) and ar/R regions (A197, I236, A245, R251) (Fig. 8). The amino acid residue at the H5 position (I236) of the ar/R filters of AtNIP5;1, which is the same as that of BnaA3.NIP5;1 (Fig. 8), plays a key role in the permeability of root cells to B (Mitani-Ueno et al. 2011). Therefore, BnaA3.NIP5;1 likely functions as a B influx transporter similar to AtNIP5;1 in B. napus, which is responsible for Befficient uptake in the roots. Post-transcriptional control of mRNA stability by the 5′ UTR can be beneficial, which provides a swift response to adjust to the environmental changes required for normal growth and development (Bhat et al. 2004). The 5′ UTR of AtNIP5;1 is known to play a key role in the induction of AtNIP5;1 transcripts under B-limitation conditions and in post-transcriptional mRNA degradation during acclimation to high-B environments to avoid toxicity (Tanaka et al. 2011). The short sequences (+184 to +197) harbouring the upstream open reading frames (uORFs) within the 5′ UTR, which is important in the regulation of AtNIP5;1 expression in response to low B (Tanaka et al. 2011), are also highly conserved in other diverse plant species (Fig. 7e). In addition, the 5′ UTR of DTE1/OsNIP3;1, which is orthologous to AtNIP5;1, has been identified as the main factor controlling expression levels in response to B limitation conditions (Liu et al. 2015). Furthermore, a sequence analysis of the 5′ UTR revealed that allelic polymorphisms are conserved between the two B-efficient (QY10 and ZS11) and two B-inefficient (W10 and Bakow) B. napus cultivars (Fig. 7c). Thus, we postulated that the 5′ UTR may play a crucial role in the regulation of NIP5;1-like gene expression, including that of the candidate gene BnaA3.NIP5;1. To further study the potential regulation sites of BnaA3. NIP5;1, we used the PLACE program to identify the underlying cis-acting regulatory DNA elements around the polymorphic sites within the promoter region (including the 5′ UTR). Around all of the polymorphic sites in the 5′ UTR (Fig. 7c), we identified five cis-acting elements; however, only a binding core sequence (ACACNNG) of a novel class of bZIP transcription factors at the position +328 (S6: T/C) has been reported to be involved in the signalling and responses to various abiotic/biotic stresses (Michelmore & Meyers 1998; Uno et al. 2000), which may be also implicated in the response to B deficiency. In addition, by searching a 1.5-kb sequence upstream the 465-bp 5′ UTR in the promoter region obtained from the WGS, we identified an InDel polymorphism at the position 737 (D1: TA/T) and three SNPs at the positions 19 (S1: A/G), 56 (S2: T/C) and 152 (S3: C/G); however, any valuable cis-acting elements in response to environmental stresses were not characterized. Combining the analyses of allelic polymorphisms and cis-acting elements in the promoter mentioned earlier, we assumed that the 5′ UTR may function as the key regulator implicated in the differential response to B deficiency in B. napus genotypes, which remains to be further validated.

1616 Y. Hua et al.

CONCLUSIONS In the present research, to facilitate our understanding of the genetic bases and molecular mechanisms underlying B efficiency in allotetraploid rapeseed, we first revealed an NIP gene that underlies the major B-efficiency QTL qBEC-A3a, and this finding potentially promotes breeding programmes for B-efficient rapeseed germ plasm in the agricultural industry. An integration of QTL mapping and comparative genomics with transcriptomic analyses considerably contributes to the rapid identification of QTGs in plant species with complex genomes.

ACKNOWLEDGMENTS This work was funded by the National Natural Science Foundation of China (Grant Nos. 31572185, 31372129) and the National Key Research and Development Program of China. The authors are also grateful to Dr Qingyong Yang (College of Informatics, Huazhong Agricultural University) for providing assistance in the analyses of WGS and DGE profiling.

REFERENCES Asmann Y.W., Klee E.W., Thompson E.A., Perez E.A., Middha S., Oberg A.L., … Kocher J.P. (2009) 3′ Tag digital gene expression profiling of human brain and universal reference RNA using Illumina Genome Analyser. BMC Genomics 10, 531. doi: 10.1186/1471-2164-10-531. Bailey T.L., Boden M., Buske F.A., Frith M., Grant C.E., Clementi L., … Noble W.S. (2009) MEME Suite: tools for motif discovery and searching. Nucleic Acids Research 37, 202–208. Bhat S., Tang L., Krueger A.D., Smith C.L., Ford S.R., Dickey L.F. & Peyraeck M.E. (2004) The Fed-1 (CAUU)4 element is a 5′ UTR dark-responsive mRNA instability element that functions independently of dark-induced polyribosome dissociation. Plant Molecular Biology 56, 761–773. Bolaños L., Lukaszewski K., Bonilla I. & Blevins D. (2004) Why boron? Plant Physiology and Biochemistry 42, 907–912. Brown P.H. & Shelp B.J. (1997) Boron mobility in plants. Plant and Soil 193, 85–101. Burland T.G. (2000) DNASTAR’s Lasergene sequence analysis software. Methods in Molecular Biology 132, 71–91. Chalhoub B., Denoeud F., Liu S.Y., Parkin I.A.P., Tang H.B., Wang X.Y., … Wincker P. (2014) Early allopolyploid evolution in the post-Neolithic Brassica napus oilseed genome. Science 345, 950-953. Chatterjee M., Tabi Z., Galli M., Malcomber S., Buck A., Muszynski M. & Gallavottia A. (2014) The boron efflux transporter ROTTEN EAR is required for maize inflorescence development and fertility. The Plant Cell 26, 2978–2995. Cheng F., Liu S.Y., Wu J., Fang L., Sun S.L., Liu B., … Wang X.W. (2011) BRAD, the genetics and genomics database for Brassica plants. BMC Plant Biology 11, 136. Crooks G.E., Hon G., Chandonia J.M. & Brenner S.E. (2004) WebLogo: a sequence logo generator. Genome Research 14, 1188–1190. Das S., Upadhyaya H.D., Bajaj D., Kujur A., Badoni S., Laxmi V., … Parida S.K. (2015) Deploying QTL-seq for rapid delineation of a potential candidate gene underlying major trait-associated QTL in chickpea. DNA Research 22, 193-203. Durbak A.R., Phillips K.A., Pike S., O’Neill M.A., Mares J., Gallavotti A., … McSteena P. (2014) Transport of boron by the tassel-less1 aquaporin is critical for vegetative and reproductive development in maize. The Plant Cell 26, 2978-1995. Eisen M.B., Spellman P.T., Brown P.O. & Botstein D. (1998) Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences of the United States of America 95, 14863–14868. Fu T.D. (2004) The present and future of rapeseed quality improvement. Journal of Huazhong Agricural University 34, 1–4. Gasteiger E., Hoogland C., Gattiker A., Duvaud S., Wilkins M.R., Appel R. D. & Bairoch A. (2005) Protein identification and analysis tools on the

ExPASy Server. In The Proteomics Protocols Handbook (ed Walker J. M.), pp. 571–607. Humana Press. Goldbach H.E., Yu Q., Wingender R., Schulz M., Wimmer M., Findeklee P. & Baluška F. (2001) Rapid response reactions of roots to boron deprivation. Journal of Plant Nutrition and Soil Science 164, 173–181. Goldberg S. (1997) Reactions of boron with soils. Plant and Soil 193, 35–48. Hanaoka H., Uraguchi S., Takano J., Tanaka M. & Fujiwara T. (2014) OsNIP3;1, a rice boric acid channel, regulates boron distribution and is essential for growth under boron-deficient conditions. The Plant Journal 78, 890–902. Hao Q.N., Zhou X.A., Sha A.H., Wang C., Zhou R. & Chen S.L. (2011) Identification of genes associated with nitrogen-use efficiency by genome-wide transcriptional analysis of two soybean genotypes. BMC Genomics 12, 525. Higo K., Ugawa Y., Iwamoto M. & Korenaga T. (1999) Plant cis-acting regulatory DNA elements (PLACE) database. Nucleic Acids Research 27, 297–300. Horton P., Park K.J., Obayashi T., Fujita N., Harada H., Adams-Collier C.J. & Nakai K. (2007) WoLF PSORT: protein localization predictor. Nucleic Acids Research 35, W585–W587. Huang X.H., Feng Q., Qian Q., Zhao Q., Wang L., Wang A.H., … Han B. (2009) High-throughput genotyping by whole-genome resequencing. Genome Research 19, 1068-1076. Illa-Berenguer E., Van Houten J., Huang Z. & van der Knaap E. (2015) Rapid and reliable identification of tomato fruit weight and locule number loci by QTL-seq. Theoretical and Applied Genetics 128, 1329–1342. Jeong I.S., Yoon U.H., Lee G.S., Ji H.S., Lee H.J., Han C.D., … Kim T.H. (2013) SNP-based analysis of genetic diversity in anther-derived rice by whole genome sequencing. Rice (N Y) 6, 6. Kajikawa M., Fujibe T., Uragchi S., Miwa K. & Fujiwra T. (2011) Expression of the Arabidopsis borate efflux transporter gene, AtBOR4, in rice affects the xylem loading of boron and tolerance to excess boron. Bioscience Biotechnology and Biochemistry 75, 2421–2423. Kelley L.A., Mezulis S., Yates C.M., Wass M.N. & Sternberg M.J. (2015) The Phyre2 web portal for protein modelling, prediction and analysis. Nature Protocols 10, 845–858. Kohl M., Wiese S. & Warscheid B. (2011) Cytoscape: software for visualization and analysis of biological networks. Methods in Molecular Biology 696, 291–303. Kosambi D. (1944) The estimation of map distances from recombination values. Annals of Eugenics 12, 172–175. Krogh A., Larsson B., von Heijne G. & Sonnhammer E.L. (2001) Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. Journal of Molecular Biology 305, 567–580. Kyte J. & Doolittle R.F. (1982) A simple method for displaying the hydropathic character of a protein. Journal of Molecular Biology 157, 105–132. Lee J., Izzah N.K., Jayakodi M., Perumal S., Joh H.J., Lee H.J., … Yang T.J. (2015) Genome-wide SNP identification and QTL mapping for black rot resistance in cabbage. BMC Plant Biology 15, 32. Li W.Z., Cowley A., Uludag M., Gur T., McWilliam H., Squizzato S., … Lopez R. (2015) The EMBL-EBI bioinformatics web and programmatic tools framework. Nucleic Acids Research 43, 580-584. Li Y.B., Fan C.C., Xing Y.Z., Jiang Y.H., Luo L.J., Sun L., … Zhang Q.F. (2011) Natural variation in GS5 plays an important role in regulating grain size and yield in rice. Nature Genetics 43, 1266-1269. Liu K., Liu L.L., Ren Y.L., Wang Z.Q., Zhou K.N., Liu X., … Wan J.M. (2015) Dwarf and tiller-enhancing 1 regulates growth and development by influencing boron uptake in boron limited conditions in rice. Plant Science 236, 18-28. Livak K.J. & Schmittgen T.D. (2001) Analysis of relative gene expression data -ΔΔC using real-time quantitative PCR and the 2 T Method. Methods 25, 402–408. Lordkaew S., Dell B., Jamjod S. & Rerkasem B. (2011) Boron deficiency in maize. Plant and Soil 342, 207–220. Lu H., Lin T., Klein J., Wang S., Qi J., Zhou Q., … Huang S.W. (2014) QTL-seq identifies an early flowering QTL located near Flowering Locus T in cucumber. Theoretical and Applied Genetics 127, 1491-1499. Marschner H. (1995) Mineral nutrition of higher plants. Academic Press, Lodon, UK. Meyer M. (2009) Rapeseed oil fuel-the crisis-proof home-made eco-fuel. Agrarforschung 16, 262–267. Mi H.Y., Lazareva-Ulitsky B., Loo R., Kejariwal A., Vandergriff J., Rabkin S., … Thomas P.D. (2005) The PANTHER database of protein families, subfamilies, functions and pathways. Nucleic Acids Research 33, D284-D288. Michelmore R.W. & Meyers B.C. (1998) Clusters of resistance genes in plants evolve by divergent selection and a birth-and-death process. Genome Research 8, 1113–1130. Mitani-Ueno N., Yamaji N., Zhao F.J. & Ma J.F. (2011) The aromatic/arginine selectivity filter of NIP aquaporins plays a critical role in substrate selectivity for silicon, boron, and arsenic. Journal of Experimental Botany 62, 4391–4398.

© 2016 John Wiley & Sons Ltd, Plant, Cell and Environment, 39, 1601–1618

Fine mapping a boron efficiency gene in rapeseed 1617 Miwa K. & Fujiwara T. (2010) Boron transport in plants: co-ordinated regulation of transporters. Annals of Botany 105, 1103–1108. Miwa K., Wakuta S., Takada S., Ide K., Takano J., Naito S., … Fujiwara T. (2013) Roles of BOR2, a boron exporter, in cross linking of rhamnogalacturonan II and root elongation under boron limitation in Arabidopsis. Plant Physiology 163, 1699-1709. Murray M.G. & Thompson W.F. (1980) Rapid isolation of high molecular weight plant DNA. Nucleic Acids Research 8, 4321–4325. O’Neill M.A., Eberhard S., Albersheim P. & Darvill A.G. (2001) Requirement of borate cross-linking of cell wall rhamnogalacturonan II for Arabidopsis growth. Science 294, 846–849. ® van Ooijen J. (2006) Joinmap : software for the calculation of genetic linkage maps in experimental populations, version 4. Wageningen, the Netherlands: KyazmaBV. Rouge P. & Barre A. (2008) A molecular modelling approach defines a new group of Nodulin 26-like aquaporins in plants. Biochemical and Biophysical Research Communications 367, 60–66. Ryabko B.Y., Stognienko V.S. & Shokin Y.I. (2004) A new test for randomness and its application to some cryptographic problems. Journal of Statistical Planning and Inference 123, 365–376. Secco D., Jabnoune M., Walker H., Shou H., Wu P., Poirier Y. & Whelan J. (2013) Spatio-temporal transcript profiling of rice roots and shoots in response to phosphate starvation and recovery. The Plant Cell 25, 4285–4304. Shorrocks V.M. (1997) The occurrence and correction of boron deficiency. Plant and Soil 193, 121–148. Stange M., Utz H.F., Schrag T.A., Melchinger A.E. & Wurschum T. (2013) High-density genotyping: an overkill for QTL mapping? Lessons learned from a case study in maize and simulations. Theoretical and Applied Genetics 126, 2563–2574. Takagi H., Abe A., Yoshida K., Kosugi S., Natsume S., Mitsuoka C., … Terauchi R. (2013) QTL-seq: rapid mapping of quantitative trait loci in rice by whole genome resequencing of DNA from two bulked populations. The Plant Journal 74, 174-183. Takano J., Miwa K., Yuan L., von Wiren N. & Fujiwara T. (2005) Endocytosis and degradation of BOR1, a boron transporter of Arabidopsis thaliana, regulated by boron availability. Proceedings of the National Academy of Sciences of the United States of America 102, 12276–12281. Takano J., Noguchi K., Yasumori M., Kobayashi M., Gajdos Z., Miwa K., … Fujiwara T. (2002) Arabidopsis boron transporter for xylem loading. Nature 420, 337-340. Takano J., Wada M., Ludewig U., Schaaf G., von Wiren N. & Fujiwara T. (2006) The Arabidopsis major intrinsic protein NIP5;1 is essential for efficient boron uptake and plant development under boron limitation. The Plant Cell 18, 1498–1509. Tamura K., Stecher G., Peterson D., Filipski A. & Kumar S. (2013) MEGA6: Molecular Evolutionary Genetics Analysis Version 6.0. Molecular Biology and Evolution 30, 2725–2729. Tanaka M., Takano J., Chiba Y., Lombardo F., Ogasawara Y., Onouchi H., Naito S. & Fujiwara T. (2011) Boron-dependent degradation of NIP5;1 mRNA for acclimation to excess boron conditions in Arabidopsis. The Plant Cell 23, 3547–3559. Tanaka M., Wallace I.S., Takano J., Roberts D.M. & Fujiwara T. (2008) NIP6;1 is a boric acid channel for preferential transport of boron to growing shoot tissues in Arabidopsis. The Plant Cell 20, 2860–2875. Trapnell C., Roberts A., Goff L., Pertea G., Kim D., Kelley D.R., … Pachter L. (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nature Protocols 7, 562-578. Trapnell C., Williams B.A., Pertea G., Mortazavi A., Kwan G., van Baren M.J., … Pachter L. (2010) Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnology 28, 511-515. Uno Y., Furihata T., Abe H., Yoshida R., Shinozaki K. & Yamaguchi-Shinozaki K. (2000) Arabidopsis basic leucine zipper transcription factors involved in an abscisic acid-dependent signal transduction pathway under drought and high-salinity conditions. Proceedings of the National Academy of Sciences of the United States of America 97, 11632–11637. Wallace I.S., Choi W.G. & Roberts D.M. (2006) The structure, function and regulation of the nodulin 26-like intrinsic protein family of plant aquaglyceroporins. Biochimica et Biophysica Acta (BBA) – Biomembranes 1758, 1165–1175. Wang Y.H., Shi L., Cao X.Y. & Xu F.S. (2007) Studies on plant boron nutrition and boron fertilization in China. In: Xu F.S., Goldbach H.E., Brown P.H., Bell R.W., Fujiwara T., Hunt C.D., … Shi L. (2007) Advances in plant and animal boron nutrition. the Netherlands: Springer Press. pp. 93–101. Warrington K. (1923) The effect of boric acid and borax on the broad bean and certain other plants. Annals of Botany 37, 457–466.

© 2016 John Wiley & Sons Ltd, Plant, Cell and Environment, 39, 1601–1618

Xu F.S., Wang Y.H. & Meng J.L. (2001) Mapping boron efficiency gene(s) in Brassica napus using RFLP and AFLP markers. Plant Breeding 120, 319–324. Yang L. (2012) Study on mechanism of boron uptake and transportation in Brassica napus cultivars with different boron efficiency. Master’s degree dissertation in Huazhong Agricultural University. pp. 35–42. Yang L., Zhang Q., Dou J.N., Li L., Guo L., Shi L. & Xu F.S. (2013) Characteristics of root boron nutrition confer high boron efficiency in Brassica napus cultivars. Plant and Soil 371, 95–104. Yang Q., Zhang D.F. & Xu M.L. (2012) A sequential quantitative trait locus finemapping strategy using recombinant-derived progeny. Journal of Integrative Plant Biology 54, 228–237. Zhang D.D., Hua Y.P., Wang X.H., Zhao H., Shi L. & Xu F.S. (2014) A highdensity genetic map identifies a novel major QTL for boron efficiency in oilseed rape (Brassica napus L.). PLoS One 9, e112089. doi: 10.1371/journal. pone.0112089. Zhao H., Shi L., Duan X.L., Xu F.S., Wang Y.H. & Meng J.L. (2008) Mapping and validation of chromosome regions conferring a new boron-efficient locus in Brassica napus. Molecular Breeding 22, 495–506. Zhao Z.K., Wu L.K., Nian F.Z., Ding G.D., Shi T.X., Zhang D.D., … Meng J.L. (2012) Dissecting quantitative trait loci for boron efficiency across multiple environments in Brassica napus. PLoS One 7, e45215. doi: 10.1371/journal. pone.0045215.

Received 22 October 2015; received in revised form 17 February 2016; accepted for publication 23 February 2016

SUPPORTING INFORMATION Additional Supporting Information may be found in the online version of this article at the publisher’s website:

Figure S1. Total biomass (dry weight) investigated in a randomly selected BC4F2 population cultivated by hydroponics under B-limited conditions. (a) Genotype of a heterozygous near-isogenic line (NIL; ZA-19) harbouring qBEC-A3a used for the segregation analysis. (b) Frequency distribution of the total dry weight in the BC4F2 population. (c) Mean total dry weight in the BC4F2 population. White, black and grey column bars indicate the NILs carrying W10 homozygous, QY10 homozygous and W10/QY10 heterozygous alleles in the QTL region harbouring qBEC-A3a, respectively. Figure S2. Differences in the root architectures of near-isogenic lines (NILs) under B deficiency (0.25 μM). Differences in the total root length (a), root surface area (b) and root volume (c) were compared among the QY10, W10, NILQ and NILW seedlings, which were cultivated in a hydroponic culture for 20 d under B deficiency (0.25 μM). NILQ and NILW represent the NILs carrying homozygous alleles from QY10 and W10 in the QTL region harbouring qBEC-A3a, respectively. The significance level was set at a P value < 0.05. Bars denote the means (n = 3), and error bars denote the standard deviation (SD). Figure S3. Overall view of the viability of suspension cells from near-isogenic lines (NILs). Overall view of the viability of suspension cells from NILQ (a) and NILW (b) under highB conditions (50 μM) and NILQ (c) and NILW (d) under lowB conditions (0.1 μM); the cells were cultivated for 8 d in the B5 medium and analysed by in situ fluorescence microscopy, with the results showing that the suspension cells remained viable (green) or died (red) (n = 10). Scale bar = 50 μm. NILQ and NILW represent the NILs carrying homozygous alleles from QY10 and W10 in the QTL region harbouring qBEC-A3a, respectively. Figure S4. Linearity analysis between the local genetic linkage map (cM) and the corresponding physical map (Mb). (a) Local genetic linkage map based on 1349 BC4F3 individual plants, which were genotyped by 14 insertion/deletion

1618 Y. Hua et al. (InDel) molecular markers developed from whole-genome re-sequencing (WGS). (b) Physical map based on the physical genomic location of the molecular markers, which were identified by the alignment of the marker sequence to the Brassica napus ‘Darmor-bzh’ genome. Figure S5. The digital gene expression (DGE) profiling results for BnaA3.NIP5;1. The expression levels of BnaA3. NIP5;1 in the shoots and the roots of QY10, NILQ and W10. NILQ denotes the homozygous near-isogenic lines (NILs) carrying qBEC-A3a. The significance level was set at a P value < 0.05. Bars denote the means (n = 3), and error bars denote the standard deviation (SD). Figure S6. Physical mapping of BnaNIP5;1s in the genome of Brassica napus. The homologous genes of BnaA3.NIP5;1 in genome-wide Brassica napus were identified by a BLASTn search of the Brassica database (BRAD) by using the candidate gene BnaA03g24370D sequence. The candidate gene BnaA3.NIP5;1 (BnaA03g24370D) is denoted by red. Figure S7. Micro-collinearity analysis of genes flanked by BnaA3.NIP5;1 in Brassicaceae species, including Arabidopsis thaliana (A. thaliana), Brassica rapa (B. rapa), Brassica oleracea (B. oleracea) and Brassica napus (B. napus). Homologous genes are connected by lines, and homologs of NIP5;1 in Brassicaceae species are indicated by

red circles. Figure S8. Analysis of the differentially expressed genes (DEGs) between NILQ and W10. (a) Venn diagram showing the number of DEGs that were overlapped between NILQ (N) and W10 (W) in the shoot (S) and the root (R). (b) Go analysis (protein class) of the DEGs in the root with expression levels that were significantly higher than those of W10. The transporter protein class is denoted by a white column. Figure S9. Genome collinearity between Arabidopsis and Brassica napus. The Y-axis chromosome indicates chromosome A3 of B. napus (rape), and the X-axis chromosomes denote the chromosomes (1–5) of Arabidopsis thaliana. Axis metrics are denoted in genes. Syntenic regions between Arabidopsis and B. napus are outlined by boxes. Table S1. Segregation analysis of qBEC-A3a in a BC4F2 population cultivated under hydroponics Table S2. Primer sequences used in this research Table S3. Annotated genes in the quantitative trait locus (QTL) region spanning 119 kb on chromosome A3 Table S4. Overview of reads generated from Illumina HiSeq 2500 for digital gene expression (DGE) profiling Table S5. Detailed sequences of the logo motifs identified by MEME in nodulin 26-like intrinsic protein (NIP) family members.

© 2016 John Wiley & Sons Ltd, Plant, Cell and Environment, 39, 1601–1618