Exploring the basis of 2-propenyl and 3-butenyl glucosinolate synthesis by QTL mapping and RNA-sequencing in Brassica juncea.

Brassica juncea is used as a condiment, as vegetables and as an oilseed crop, especially in semiarid areas. In the present study, we constructed a genetic map using one recombinant inbred line (RIL) of B. juncea. A total of 304 ILP (intron length polymorphism) markers were mapped to 18 linkage groups designated LG01-LG18 in B. juncea. The constructed map covered a total genetic length of 1671.13 cM with an average marker interval of 5.50 cM. The QTLs for 2-propenyl glucosinolates (GSLs) colocalized with the QTLs for 3-butenyl GSLs between At1g26180 and BnapPIP1580 on LG08 in the field experiments of 2016 and 2017. These QTLs accounted for an average of 42.3% and 42.6% phenotypic variation for 2-propenyl and 3-butenyl GSLs, respectively. Furthermore, the Illumina RNA-sequencing technique was used to excavate the genes responsible for the synthesis of GSLs in the siliques of the parental lines of the RIL mapping population, because the bulk of the seed GSLs might originate from the siliques. Comparative analysis and annotation by gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) revealed that 324 genes were involved in GSL metabolism, among which only 24 transcripts were differentially expressed genes (DEGs). Among those DEGs, 15 genes were involved in the biosynthesis and transport of aliphatic GSLs, and their expression patterns were further validated by qRT-PCR analysis. Joint QTL mapping and RNA-sequencing analyses reveal one candidate gene of IIL1 (LOC106416451) for GSL metabolism in B. juncea. These results will be helpful for further fine mapping, gene cloning and genetic mechanisms of 2-propenyl and 3-butenyl GSLs in B. juncea.

Introduction

Brassica juncea (AABB, 2n = 36) is an important allotetraploid species that originated from interspecific hybridization between B. rapa (AA, 2n = 20) and B. nigra (BB, 2n = 16) followed by chromosome doubling in the natural environment. The crop exhibited better drought and heat tolerance, disease resistance, insect resistance and shattering resistance than B. napus [1-7]. In addition to its use as a condiment in Canada and China and as a vegetable in China, great efforts have been made to develop B. juncea as an alternative oilseed crop, especially in the semiarid areas. Canola-quality B. juncea with less than 2% erucic acid in the seed oil and less than 30-uM GSLs/g of the deoiled cake was developed through a cross between a zero erucic B. juncea line and a low GSLs B. juncea line [8]. Hybrid breeding has been successfully used to enhance the yield potential in canola B. napus. In B. juncea, the Ogura cytoplasmic male sterility (cms) and its restorer gene (Rfo) were introduced for heterosis utilization [9,10]. Then, the B. juncea Ogura cms restorer line (RfoRfo) was improved with drastically reduced linkage drag, good seed set and high agronomic performance by hybridization with resynthesized B. juncea lines and subsequent molecular marker-assisted selection in B. juncea [11].

Glucosinolates (GSLs) were first discovered in mustard seeds during an exploration of the chemical origin of their sharp taste in the 17th century. To date at least 120 different GSLs have been identified in sixteen families of dicotyledonous angiosperms [12]. In the Brassicaceae family, GSLs are the major secondary metabolites and could be synthesized in all species of this family by a three-part biosynthetic pathway from methionine, tryptophan and phenylalanine [13-15]. GSLs are mainly divided into aliphatic, indolic and benzyl GSLs in Brassica species. Most of the tissues, such as rosette leaves, roots, seeds, inflorescences, contain GSLs. The GSL contents of different tissues are not entirely synthesized locally. The transport of GSLs was suggested more than 40 years ago through a number of studies that indicated that GSLs are produced in maternal tissue and subsequently transported to the seed [16,17]. This could explain why the GSL profiles of hybrid seeds were similar to those of the maternal plants instead of being intermediate between those of the maternal and paternal plants [16]. As the closest organ to the seeds, the silique is the only organ to produce all the GSLs found in the seed [17-19]. Thus, the siliques might be the most important source of seed GSLs.

In seeds of the Brassicaceae, aliphatic GSLs were the major type of GSLs, while 2-propenyl and 3-butenyl GSLs were the major types of aliphatic GSLs. Previous studies have shown that the European type of B. juncea mainly contains 2-propenyl GSLs and Indian types contain both 2-propenyl and 3-butenyl GSLs [20-22]. The inheritance of 2-propenyl and 3-butenyl GSLs was studied in F1, F2 and backcrossing populations of B. juncea, indicating that these two characteristics had maternal effects and might be controlled by multiple additive alleles at the same loci [21]. Using bulked segregant analysis, one ISSR marker was found to be tightly linked to high 2-propenyl GSLs in B. juncea and converted to a SCAR marker [23]. A total of 17 metabolic QTLs for the genetic control of 2-propenyl and 3-butenyl GSLs were identified on LG2, LG3, LG4, LG5, LG6, LG7, LG8 and LG9 in B. oleracea, among which 12 regulated 2-propenyl and 3-butenyl GSLs at the same time, 2 were specific for 2-propenyl GSLs and 3 were specific for 2-propenyl GSLs [24]. In B. napus seeds, 3 QTLs on A9 and 1 QTL on C2 for the genetic regulation of 3-butenyl GSLs were detected, and each could explain a phenotypic variation between 5.0% and 14.8% [25].

ILP markers utilize variations in intron sequences and are the most easily recognizable type of marker, as they can be detected by PCR with primers designed for the exons flanking the target intron [26]. Furthermore, ILP markers are unique because they are gene-specific, codominant, hypervariable, neutral, convenient and reliable [26,27]. These markers have been used for genetic analysis in many species, such as rice, yellow mustard, foxtail millet, maize, tomato, B. juncea, B. rapa and Arabidopsis [26-32]. In the present study, we successfully used PCR-based ILP markers for the development of a genetic map based on one RIL mapping population in B. juncea. Furthermore, we detected one novel major QTL for 2-propenyl and 3-butenyl GSLs on LG08 of the B. juncea genome. In addition, we also attempted to explore the mechanism resulting in the variation in seed GSL contents by RNA-sequencing of siliques from the parental lines of the RIL mapping population.

Materials and methods

Plant materials and field trial

Parents G266 and G302 are DH lines. The seeds of the G266 line had low 2-propenyl and high 3-butenyl GSL contents, while G302 had high 2-propenyl and low 3-butenyl GSL contents. The RILs produced by G266×G302 displayed great variation in agronomic traits, such as flowering time and number of seeds per silique as presented in our earlier study [33]. Three replicates for each of the parental lines G266 and G302, their F1 and 167 F6 RILs were planted on the farm of Guizhou University, Guiyang, China in 2016 and 2017. The design of the trial was a randomized complete block. Each plot consisted of two rows with one size of 3.66 m2 (3 m×1.22 m). Five grams of seeds from three plants in each plot were harvested at maturity and analyzed for 2-propenyl and low 3-butenyl GSL contents. The average 2-propenyl and low 3-butenyl GSL contents of the three replicates were used for QTL analysis.

DNA extraction and polymerase chain reaction (PCR)

Genomic DNA was extracted from young leaves of the parental lines, F1 and 167 F6 RILs using the modified sodium dodecyl sulfate method [34]. PCR of the ILP markers was carried out according to our previous studies [11,35]. Each PCR (20 μl) contained 1× standard PCR buffer (NEB), 1 U of Taq polymerase (NEB), 0.25 μM forward primer, 0.25 μM reverse primer, 100 μM each dNTP and 50 ng of genomic DNA in a total volume of 20 μL. The PCR amplification consisted of an initial denaturation at 94°C for 5 min; 35 cycles consisting of 94°C (45 sec); 55°C (45 sec) and 72°C (1 min); followed by termination at 72°C for 7 min. All PCR products were analyzed by electrophoresis in 2% agarose gels in 1× tri-acetate-ethylene diaminetetra acetic acid buffer. Gels were visualized by staining in ethidium bromide and photographed on a digital gel documentation system.

Construction of genetic linkage map and QTL analysis

The genetic linkage map of B. juncea was constructed by using JoinMap 4.0 software at LOD≥4.0 [36]. Recombination frequencies were converted to map distances in cm using the Kosambi mapping function and the genetic map was drawn with MapChart software [37]. QTL analysis of 2-propenyl and 3-butenyl GSL contents was performed using the interval mapping method of MapQTL 6.0 software [38]. A permutation test (1,000 replications) was used to determine the significance level for LOD with a genome-wide probability of p<0.05.

Glucosinolate component analysis

The 2-propenyl and 3-butenyl GSL contents of the mature seeds from each plot were analyzed following published methods [39,40] with minor modifications. Each seed sample was crushed and 200 mg of each sample was extracted twice with 2 ml boiling 70% methanol. The concentration of GSLs in the seeds was determined by high-performance liquid chromatography (Waters 2487⁄600⁄717) using the ISO9167-1 (1992) standard method.

RNA extraction, preparation, sequencing and data analysis

Total RNA (2 μg) was extracted from fresh seed coats of 20 DAP (days after pollination) siliques of three independent plants for each of the parental lines G266 and G302 using the TRIzol kit (Invitrogen, Carlsbad, CA), according to the manufacturer’s instructions. The RNA purity was checked using the Kaiao K5500®Spectrophotometer (Kaiao, Beijing, China), and the RNA integrity and concentration were assessed using the RNA Nano 6000 Assay Kit for the Bioanalyzer 2100 system (Agilent Technologies, CA, USA). Then, the six RNA samples were sent to the ANOROAD GENOME company (http://www.genome.cn/) for the construction of cDNA libraries and Illumina deep sequencing according to the paper of Wang et al. [41]. The raw RNA-sequencing data were filtered by a Perl script, following the steps of Wu et al. [42].

Identification and annotation of differentially expressed genes (DEGs)

DESeq2 v1.6.3 was designed for differential gene expression analysis between two samples with three biological replicates under the theoretical basis obeys the hypothesis of negative binomial distribution for the value of count. The p-value was corrected by the BH method. Genes with q≤0.05 and |log2_ratio|≥1 were identified as differentially expressed genes (DEGs) [43]. The DEGs obtained were further annotated with Gene Ontology (GO, http://geneontology.org/) and analyzed by KEGG (Kyoto Encyclopedia of Genes and Genomes, http://www.kegg.jp/) [44,45]. The GO enrichment of DEGs was implemented by the hypergeometric test, in which the p-value is calculated and adjusted to produce the q-value, and the data background is the genes in the whole genome. GO terms with q<0.05 were considered to be significantly enriched. GO enrichment analysis was used to determine the biological functions of the DEGs. KEGG is a database resource containing a collection of manually drawn pathway maps representing our knowledge of molecular interaction and reaction networks. The KEGG enrichment of the DEGs was determined by the hypergeometric test, in which p-value was adjusted by multiple comparisons to produce the q-value. KEGG terms with q<0.05 were considered to be significantly enriched.

Quantitative real time-PCR (qRT-PCR) analysis

Quantitative real-time PCR (qRT-PCR) was used to verify the transcript levels of the RNA-Seq results. Total RNA was extracted using the TRIzol kit (Invitrogen), according to the manufacturer’s instructions. Then, the cDNA was synthesized by reverse transcription using PrimeScript RT reagent kits with gDNA Eraser (Takara, Dalian, China) according to the manufacturer’s instructions. Sixteen gene-specific primers for qRT-PCR were designed based on reference unigene sequences randomly chosen from the DEGs using Primer Premier 5.0. Real-time PCR was conducted using SsoAdvancedTM Universal SYBRGreen Supermix (Hercules, CA) in a typical 20 μl PCR mixture. The 20 μl mixture contained 10 μl SYBR Green Supermix (2×), 0.4 μl reverse and forward primers (10 μM), 2 μl (100 ng) template cDNA, and 7.2 μl ddH2O. The qRT-PCR conditions were 95°C for 2 min, followed by 40 cycles of 95°C for 10 s (denaturation), followed by 60°C for 20 s (annealing and extension). The 2-ΔΔCt algorithm was used to calculate the relative level of gene expression. The β-actin gene was used as the internal control, and the T399 samples served as the control. All qRT-PCR were performed with three biological replicates, and run on a Bio-Rad CFX96 Real Time System (Bio-Rad, Hercules, CA, USA).

Results

Polymorphism between the parental lines G266 and G302

A total of 1,272 ILP primers, 284 from Arabidopsis thaliana [32], 745 from B. napus and 243 from B. rapa available in the Potential Intron Polymorphism (PIP) database [27], were used to screen the parental lines G266 and G302 for polymorphic primers. Of the 1,272 ILP primers, 306 (24.1%) generated clear and scorable polymorphic bands between the parental lines varying in size from 150 to 1250 bp. Among the 306 polymorphic primers, 266 (86.9%) amplified one locus, 35 (12.4%) produced two loci, 4 (1.4%, At4g11790, At1g07980, PIP1848 and At3g52990) produced three loci, 2 (0.7%, PIP1202 and PIPR68) revealed four loci and one (0.4%, At1g72890) revealed five loci. In summary, 359 polymorphic markers were amplified by 306 polymorphic primers, including 231 dominant ones and 128 codominant ones. The 359 polymorphic markers were used to construct the linkage map with the RIL population of G266×G302 in Brassica juncea.

Construction of one genetic linkage map

A total of 304 polymorphic loci of the 359 polymorphic markers (84.7%) were mapped on 18 linkage groups and covered a genetic length of 1671.13 centiMorgans (cM) with an average marker interval of 5.50 cm (Table 1 and Figs 1 and 2). The linkage groups were designated as LG01-LG18.

Table 1

Characterization of the 18 linkage groups in Brassica juncea.

Linkage Group	Map Length (cM)	Marker interval (cM)			No. of markers
		Average	Max Distance (cM)	Min Distance (cM)
LG01	88.37	5.52	24.19	0.06	17
LG02	64.38	5.36	13.79	0.35	13
LG03	134.25	6.39	19.30	0.01	22
LG04	82.05	8.21	37.39	0.33	11
LG05	61.31	1.92	20.96	0.22	33
LG06	77.10	7.71	33.53	0.00	10
LG07	93.83	11.73	25.79	0.77	9
LG08	180.48	5.31	15.07	0.00	35
LG09	80.35	3.21	16.21	0.19	26
LG10	75.00	7.50	16.21	0.83	11
LG11	83.13	7.56	13.69	0.74	12
LG12	80.25	10.03	16.62	5.10	9
LG13	78.53	8.73	16.85	2.77	10
LG14	135.16	5.88	14.43	0.01	24
LG15	94.46	4.11	24.01	0.04	24
LG16	54.41	4.53	13.02	0.60	13
LG17	97.63	7.51	20.67	0.10	14
LG18	110.44	11.04	26.26	0.62	11
Total	1671.13	-	-	-	304

Fig 1

The 9 linkage groups from LG01 to LG09 in Brassica juncea.

For each linkage group, the ILP markers were shown on the right side and the marker position in centiMorgan on the left side.

Fig 2

The 9 linkage groups from LG10 to LG18 in Brassica juncea.

For each linkage group, the ILP markers were shown on the right side and the marker position in centiMorgan on the left side.

The map lengths of the 18 linkage groups ranged from 54.41 cM for LG16 to 180.48 cM for LG08 with an average of 92.843 cM. The marker interval ranged from 0.00 cM to 37.39 cM with an average of 5.75 cM. LG03, LG08, LG14 and LG18 had map lengths longer than 100 cM, ranging from 110.44 cM to 180.48 cM. LG18 had the largest average marker interval of 11.04 cM. LG08 had the longest map length of 180.48 cM and the most ILP markers (35 markers). LG07, LG15 and LG17 had similar map lengths ranging from 93.83 cM to 97.63 cM. LG01, LG04, LG09, LG11 and LG12 had similar map lengths ranging from 80.25 cM to 88.37 cM. LG04 had the largest island without markers (37.39 cM). LG10, LG06 and LG13 had similar long map lengths ranging from 75.00 cM to 78.53 cM. LG02, LG05 and LG16 had similar long map lengths ranging from 54.41 cM to 64.38 cM. LG05 had the shortest map length of 54.41 cM and the smallest average marker interval of 1.92 cM.

QTL mapping of 2-propenyl and 3-butenyl glucosinolate contents

The 167 RILs, their parental lines G266 and G302, and the F1, were grown in the field with three replications. These lines were grown at Guiyang and distributed normally in 2016 and 2017 (Fig 3). No significant difference across the two years for 2-propenyl and 3-butenyl GSL contents was detected (p = 0.594 and p = 0.888, respectively). The 2-propenyl GSL contents was significantly negatively correlated with the 3-butenyl GSL contents in 2016 (r = -0.920, p = 0.000) and 2017 (r = -0.914, p = 0.000), respectively. The parents of the population differed in 2-propenyl GSL contents, with mean values of 17.29 μmol/g and 180.90 μmol/g for G266 and G302, respectively (Table 2). The mean 2-propenyl GSL contents of F1 was 67.34 μmol/g, which was closer to that of the female parent and slightly higher than the mean value of the RIL mapping population (63.45 μmol/g) (Table 2). The range of 2-propenyl GSL contents in the RILs was 10.70~214.36 μmol/g in 2016 and 9.59~215.60 μmol/g in 2017 (Table 2). The parents of the population differed in 3-butenyl GSL contents, with mean values of 86.98 μmol/g and 15.80 μmol/g for G266 and G302, respectively (Table 2). The mean 3-butenyl GSL contents of F1 was 63.80 μmol/g, more similar to that of the female parent and slightly lower than the mean value in the RIL mapping population (75.98 μmol/g) (Table 2). The range of 3-butenyl GSL contents of the RILs was 8.71~170.63 u mol/g in 2016, and 5.82~166.04 μmol/g in 2017 (Table 2).

Fig 3

Frequency distribution of 2-propenyl and 3-butenyl glucosinolate content of the recombinant inbred lines (RILs) in 2016 and 2017.

Y-axis: the number of the corresponding recombinant inbred lines; X-axis: the glucosinolate content of 2-propenyl (left) and 3-butenyl (right) respectively (u mol/g).

Table 2

2-propenyl and 3-butenyl GSL content of the parental lines G266 and G302, F1 seeds of G266×G302 and the RIL mapping population in 2016 and 2017.

Traits(u mol/g)	Years	Parents		F1	RILs
		G266	G302		Mean	Range
2-Propenyl	2016	19.14±0.21a	181.53±7.32	73.50±1.12	65.45	10.70~214.36
	2017	15.43±0.60	180.27±11.51	61.18±1.91	61.44	9.59~215.60
	mean	17.29	180.90	67.34	63.45	˗
3-Butenyl	2016	86.79±1.00	20.68±0.16	57.66±0.89	76.42	8.71~170.63
	2017	87.17±1.15	10.91±0.63	69.93±1.20	75.53	5.82~166.04
	mean	86.98	15.80	63.80	75.98	˗

a: standard deviation.

QTL analysis was performed for 2-propenyl (SIN, sinigrin) and 3-butenyl (GNA, gluconapin) GSL contents in 2016 and 2017 respectively. Two major QTLs of SIN-2016 and SIN-2017 for 2-propenyl GSL contents (LOD = 16.46 and 16.64) were colocalized between At1g26180 and BnapPIP1580 on LG08, accounting for 42.3% and 42.6% of the total variation in 2016 and 2017, respectively (Table 3 and Fig 4). Another two QTLs for 3-butenyl GSL contents were detected and colocalized with GNA-2016 and GNA-2017 between At1g26180 and BnapPIP1580 on LG08 (Table 3 and Fig 4). The two QTLs for 3-butenyl GSL contents explained 31% and 38.4% of the total variation in 2016 and 2017 respectively (Table 3). All these QTLs were mapped to a region adjacent to the ILP marker At4g20150-1 on LG08 (Table 3 and Fig 4).

Table 3

QTLs for 2-propenyl and 3-Butenyl GSL components in the RIL mapping population derived from the cross of G266 and G302 in 2016 and 2017.

Name of the QTLsa	Chromosome	Peak Position	LODb	R2,%c	Additive effect (μmol/g seed)	Nearest ILP and its interval to the peak
SIN-2016	LG08	166.21	16.46	42.3	6.09	At4g20150-1,1.37
SIN-2017	LG08	164.21	16.64	42.6	6.25	At4g20150-1,3.37
GNA-2016	LG08	165.21	11.14	31.0	6.63	At4g20150-1,2.37
GNA-2017	LG08	165.21	14.53	38.4	0.10	At4g20150-1,2.37

a SIN: sinigrin or 2-propenyl GSL; BUT: gluconapin or 3-Butenyl GSL; 2016: the results are from the field experiment in 2016; 2017: the results are from the field experiment in 2017.

b LOD: logarithm of the odds score for QTLs calculated by composite interval mapping.

c R2: the phenotypic variation explained by a QTL in percentage.

Fig 4

QTL mapping of 2-propenyl and 3-butenyl GSLs and synteny analysis between LG08 in the present study and A08 of B. juncea and B. rapa.

The QTLs for 2-propenyl (SIN, Sinigrin) co-localized with the QTLs for 3-butenyl (GNA, Gluconapin) GSL content. 1-LOD and 2-LOD supporting intervals of each QTL were marked by thick and thin bars, respectively. Eight A. thaliana markers prefixed “At” (bold and red) on LG08 show a synteny with A08 of B. juncea in publish papers [32]. Eighteen ILP markers prefixed “Bnap and Brap” (bold and blue) on LG08 show a synteny between LG08 and A08 of B.rapa through balsting analysis with Brapa_1.0 of Brassica rapa cultivar Chiifu in NCBI (https://www.ncbi.nlm.nih.gov/).

Synteny relationships between LG08 and A08 of B. juncea and B. rapa

LG08 contained a total of 35 ILP markers. Among these, 13 (37.1%) were developed from the single-copy genes of A. thaliana, and 17 and 5 were developed from the unique transcript fragments of B. napus and B. rapa, respectively [27]. To validate the exact linkage group of LG08 with QTLs for 2-propenyl and 3-butenyl GSL contents, a synteny analysis was performed. Eight of the 13 A. thaliana markers prefixed “At” on LG08 showed synteny between LG08 and A08 of B. juncea (Fig 4). Furthermore, the sequences of the unique transcript fragments for developing the 22 B. napus and B. rapa markers were used to blast against the Brassica rapa genome (Brassica rapa cultivar Chiifu, Brapa_1.0) in NCBI (https://www.ncbi.nlm.nih.gov/), and 18 (81.8%) were mapped to the A08 chromosome (S1 Table). The synteny analysis indicated that LG08 was exactly chromosome A08 of the A genome.

Transcriptome analysis of the parental siliques as potential

GSL source for seeds

To construct a de novo transcriptome database, three RNA libraries were generated for each of G266 and G302 lines through Illumina sequencing. A total of 139,197,152 and 138,957,982 raw reads were generated from the G266 and G302 libraries, respectively (S2 Table). After removing low quality reads, adapter polluted reads and higher N contents (>5%) reads, a total of 134,953,284 (96.95%, T399) and 134,832,452 (97.03%, T085) clean reads were obtained (S2 Table). After filtering out the genes that contained only one exon or encoded short peptide chains (<50 amino acid residues), a total of 81,826 transcripts were revealed by blasting the reference genome using DESeq2 (v1.6.3). To functionally annotate those transcripts, the 81,826 transcripts were blasted in search of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Finally, 8,1694 transcripts (92.64%) were successfully annotated by GO and KEGG, and 324 ones of these transcripts were involved in GSL metabolism (S3 Table).

To identify differentially expressed genes (DEGs) involved in the GSL metabolism of siliques between G266 and G302. A rigorous algorithm with a threshold of “FDR≤0.05 and |Log2Ratio|≥1” was developed and used as thresholds to judge the significance of differences in transcript abundance. It was found that only 24 transcripts were DEGs related to GSL metabolism. Among those DEGs, 15 genes are involved in the biosynthesis and transport of aliphatic GSLs (Table 4), and 5 and 4 genes are involved in the biosynthesis of indolic GSLs and the breakdown of GSLs, respectively (S4 Table).

Table 4

The candidate genes involved in biosynthesis of and transporting of aliphatic GSLs.

SNa	Gene Name	AGI code	Unigene ID from RNA-Seq	Function
1	CYP83A1	AT4G13770	LOC106447562(U)b;LOC106391682(U)	aldoxime→s-alkyl-thiohydroximate [46–48]
2	SOT18	AT1G74090	LOC106366617(D);LOC106354324(D);LOC106436726(D)	PAPS-dependent sulfation of desulfo-GSLs→GSLs [49]
3	SOT17	AT1G18590
4	IIL1	AT4G13430	LOC106416451(U)	2-Alkyl-malic acid→3-Alkyl-malic acid [47,50]
5	IPMI2	AT2G43100	LOC106434491(U)
6	IPMI SSU1	AT2G43090
7	AOP3	AT4G03050	LOC106430050(U);LOC106438719(U);LOC106389979(U)	methylsulfinylalkyl GSL→ hydroxyalkyl GSL [47]
8	SUR1	AT2G20610	LOC106440999(D)	s-alkyl-thiohydroximate→ thiohydroximate [47,51,52]
9	MYB28	AT5G61420	LOC106382207(U);LOC106429668(U)	the whole process of biosynthesis of methionine-derived GSL [46]
10	MYB29	AT5G07690
11	MYB76	AT5G07700
12	GTR2	AT5G62680	LOC106411192(U);LOC106347844(D)	GSL transporting [53]
13	GTR1	AT3G47960

a: Serial Number.

b: “U” means up-regulated, “D” means down-regulated.

To verify the expression of these transcripts detected by RNA-Seq, the 15 candidate genes involved in the aliphatic GSL metabolism pathway were randomly chosen for validation by qRT-PCR. Detailed information about the chosen DEGs and primers is listed in S5 Table. The data obtained by qRT-PCR were consistent with the RNA-Seq results (Fig 5), suggesting the reliability of the transcriptome database.

Fig 5

Comparison of gene expression values obtained by RNA-seq and qRT-PCR.

Fold changes were calculated for 15 DEGs and a high correlation (R2 = 0.92) was observed between the results obtained using the two techniques.

Joint QTL mapping and RNA-sequencing analyses reveal the candidate genes for GSL metabolism in B. juncea

To integrate the results of QTL mapping and RNA-sequencing, we performs an alignment analysis between the 24 DEGs related to GSL metabolism and the reference genome of B. juncea [16] by the BLAST-like alignment tool [54]. Only two DEGs of LOC106429668 encoding MYB28/MYB29/MYB76 (physical position: 23,048,306) and LOC106416451 (physical position: 5,833,626) encoding IIL1 were located on A08 of B. juncea genome. In addition, the DNA sequence designed for the PIP markers on A08 also blast the B. juncea reference genome [16]. The QTLs for 2-propenyl and 3-Butenyl GSL is located between the physical position of 18,549,777 (BnapPIP592) and the start point of the chromosome, the region of which was overlapped with the position of LOC106416451.

Discussion

Recombinant inbred lines (RILs) are an important resource in the genetic mapping of complex traits in many species. The RIL mapping population was successfully produced in our laboratory, allowing us to construct a genetic linkage map by utilizing ILP markers in B. juncea. The ILP primers were designed on the conserved exons flanking the target intron of cDNA/EST sequences to exploit its polymorphism. Each ILP marker locus might represent one gene copy in the studied genome. Taking the polymorphic and monomorphic loci together, approximately 46.8% of the 306 polymorphic ILP primers in the present study revealed more than one locus, indicating a very close ratio revealed by ILP and RFLP primers in the earlier studies [55,56]. The multiple loci revealed by ILP primers confirmed the polyploidy of B. juncea. The higher polymorphism ratio of 24.1% in the ILP primers between the parental lines G266 and G302 not only revealed the hypervariability of ILP primers but also suggested a high degree of variation between the parental genomes. This high genetic difference between the parental lines would be convenient to increase the density of genetic markers in different linkage groups in B. juncea.

The 2-propenyl and 3-butenyl GSLs are the major glucosinolates found in B. juncea [57]. The colocated QTL regions of 2-propenyl and 3-butenyl GSLs on A08 of B. juncea represented one novel QTL first detected on A08 in Brassiceae [57-59] that explained average phenotypic variations of 42.5% and 42.6%, respectively. The first reason might be that fewer studies focused on 2-propenyl and 3-butenyl GSLs of the germplasm originating in China as the important center of origin in B. juncea. Second, the exact order of linkage groups was difficult to obtain before the B. juncea genome was sequenced [16]. Another reason might be recombination events between the chromosomes of B. juncea in different regions. Although QTL mapping of 2-propenyl and 3-butenyl GSLs has been performed previously in B. juncea [57-59], no convenient and reliable primers could be used for gel detection in marker-assisted breeding. In the present study, all 306 primers mapped on B. juncea were ILP-type, providing a convenient, specific and rapid detection method for agarose gel electrophoresis in marker-assisted breeding. The ILP marker At4g20150-1 on A08 had the nearest distance of 1.37–3.37 cM to the peak. The ILP primer At4g20150 produced six fragments between 400 bp and 700 bp in the present study and mostly represented six copies of one gene, among which two copies were polymorphic and mapped to LG01 (At4g20150-2) and LG08 (At4g20150-1), respectively. In another genetic map of B. juncea, the primer amplified three copies from A3, B7 and B8 [32]. The specific marker of At4g20150 was codominant, clear and simple to score by agarose gel, which resulted in one 700 bp fragment. The novel QTL and the linked marker of At4g20150-1 can be helpful in exploiting the metabolic mechanism of 2-propenyl and 3-butenyl GSLs. The marker tightly linked to QTLs can also be used for marker-assisted selection (MAS). For example, Xu et al. (2018) transferred a thermostable β-amylase from wild barley into a commercial variety, and identified several elite lines with MAS [60], and a major QTL for resistance to Fusarium head blight was transferred from Thinopyrum elongatum onto durum wheat 7AL chromosome arm by MAS [61].

GSL synthesis in seeds is nearly nonexistent, so these compounds are mainly imported from other tissues [17,62-64]. As the closest GSL source and the only organ with similar types of GSLs, the siliques might be the source of most seed GSLs in Brassicaceae. In the present study, the RNA-Seq technique was used to screen the key genes for aliphatic GSL synthesis in the siliques of the parental lines G266 and G302. In the siliques of G302, which has high aliphatic GSL contents, 9 DEGs associated with CYP83A1, IIL1, IPMI2, IPMI SSU1, AOP3, MYB28, MYB29 and MYB76 were upregulated, resulting in its high GSL contents. Surprisingly, SUR1 and SOT17-18 were downregulated for unknown reasons. Furthermore, GTR2 is upregulated in the siliques of G302, and thus, it might play a more important role in GSL transport from siliques to seeds than the downregulated gene GTR1. The process of GSL metabolism is complicated, and few advances have been achieved. The main reasons might be the transport of GSLs among different organs. The combined method of QTL mapping and RNA-Seq should be helpful for the future fine mapping and gene cloning of 2-propenyl and 3-butenyl GSLs.

The joint QTL mapping and RNA-sequencing analyses reveal one candidate gene of LOC106416451 encoding IIL1. IIL1 is mainly responsible for the isomerization of 2-alkyl-malic acid to form 3-alkyl-malic acid [47,50,65,66]. In the present study, IIL1 is significantly highly (Log2 Fold Change = 9.71, p = 3.58E-15) expressed in the siliques of G302 with high aliphatic GSLs than that in G266 with low aliphatic GSLs. The primary work validates that the IIL1 might be the key gene for GSL regulation in the present RIL mapping population. However, more work is needed to narrow the QTL region and validate the candidate gene of LOC106416451 in our future study.

In addition, the mapping population used in the present study displayed great variation in agronomic and quality traits in this study and our earlier study [33]. The constructed genetic map would be useful in QTL mapping, gene cloning and marker-based precision breeding of more important traits in B. juncea. Because the number of traditional genetic markers is limited, we would sequence the RILs to develop more SNPs (single nucleotide polymorphism) and create a unified, saturated genetic map of B. juncea in the future.

The synteny analysis between unique transcript of ILP primers and the Brapa_1.0 genome.

(DOCX)

Click here for additional data file.

(DOCX)

Click here for additional data file.

The 324 transcripts involved in GSL metabolism.

(XLSX)

Click here for additional data file.

The candidate genes involved in biosynthesis of indolic GLS.

(DOCX)

Click here for additional data file.

The primers for qRT-PCR validation.

(DOCX)

Click here for additional data file.

29 Aug 2019

[EXSCINDED]

PONE-D-19-20230

Exploring the basis of 2-propenyl and 3-butenyl glucosinolate synthesis by QTL mapping and RNA-sequencing in Brassica juncea

PLOS ONE

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Reviewer #1: This QTL work is interesting. However, the author didn`t perform a good combination between QTL and RNA-Seq data. Are there some DEGs location in the QTL regions, or all these DEGs are the downstream genes of QTL candidate gene? A small question, DEseq2 uses read count to calculate DEG instead of FPKM.

Reviewer #2: This manuscript constructed a genetic linkage map in B. juncea. Meanwhile, QTL mapping and RNA-seq were used to reveal the 2-propenyl and 3-butenyl GSLs synthesis. The results will be helpful for further fine mapping, gene cloning and genetic mechanisms of 2-propenyl and 3-butenyl GSLs in B. Juncea, which worth publishing in the PLOS one. But still some aspects need to be improved or considered in the further work：

1．Polymorphism comparison between the parental lines, makers, genetic map can be simply introduced in introduction and results; part;

2. The whole genome of B. juncea has been sequenced, how about the synteny relationships between the linkage map and sequencing results? It is better to show some comparative results because different B. juncea lines may have structural variations in certain regions on their genomes.

3. Transcriptome data analysis and processing can be briefly introduced, but with emphasis on describing transcriptome results and the differential expressed genes associated with those QTL results, especially those differential expressed genes involved into the 2-propenyl and 3-butenyl GSLs biosynthetic pathways;

4. The discussion part may be focused on the significance and application of the new QTL detected for breeding. How about the relationship between the QTL on LG08 and previous published ones?

**********

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Reviewer #1: No

Reviewer #2: Yes: Yingfen Jiang

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Submitted filename: REVIEW.docx

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10 Sep 2019

Maoteng Li

Academic Editor

PLOS ONE

September 10, 2019

PONE-D-19-20230

Exploring the basis of 2-propenyl and 3-butenyl glucosinolate synthesis by QTL mapping and RNA-sequencing in Brassica juncea

Dear professor Li,

Thank you for your email dated August 29, 2019 regarding our manuscript “Exploring the basis of 2-propenyl and 3-butenyl glucosinolate synthesis by QTL mapping and RNA-sequencing in Brassica juncea submitted to PLOS ONE.” We have accepted the viewers’ comments and revised the manuscript accordingly. Below, we have addressed those comments item by item.

1. To meet PLOS ONE’s style requirements, we have made some revisions as follows:

(1) Line 5 in the revised version – “¶” is used for the co-first author with equal contributions.

(2) Lines 12-18 in the revised version – “These authors contributed equally to this work” is moved to the position just after the e-mail address.

(3) Lines 14-15 in the revised version – “Telephone: +86-851-3855894 and Facsimile: +86-851-83621956” is deleted.

(4) Line 16 in the revised version – “address” is deleted and “(ET)” is added.

(5) Lines 42, 67, 128, 215, 394, 468, 479, 483 and 698 in the revised version –using Level 1 Heading of “Bold type and 18bp font”.

(6) Lines 129, 141, 153, 161, 168, 179-180, 197, 216, 230, 259-260, 321-322 and 335-336 in the revised version –using Level 2 Heading of “Bold type and 16bp font”.

(7) Line 233 in the revised version – “Figs 1 and 2” replaced “Figure 1 and Figure 2”.

(8) Lines 236-238, 280-283, 302-308 and 362-366 in the revised version – “Table 1”, “Table 2”, “Table 3” and “Table 4” are moved into, respectively, from Lines 677-695.

(9) Line 263 in the revised version – “Fig 3” replaced “Figure 3”.

(10) Lines 294, 297, 299 and 328 in the revised version – “Fig 4” replaced “Figure 4”.

(11) Line 332 in the revised version – “S1 Table” replaced “Supplementary S1”.

(12) Line 349 in the revised version – “S3 Table” replaced “Supplementary S2”.

(13) Line 360 in the revised version – “S4 Table” replaced “Supplementary S3”.

(14) Line 372 in the revised version – “S5 Table” replaced “Supplementary S4”.

(15) Line 373 in the revised version – “Fig 5” replaced “Figure 5”.

(16) Lines 469-477 in the revised version – the funding information of “This work was funded by the National Natural Science Foundation of China (Grant No. 31560422), Agricultural Science and Technology Support Program of Guizhou Province (Qiankehe zhicheng No. [2019]2396), Science and Technology Foundation of Guizhou Province of China (Grant No. Qiankehe J zi [2015]2052), Scientific Research Foundation for Returned Scholars, Ministry of Education of China (Grant No. Jiaowaisiliu [2015]1098), Foundation of Guizhou University (Grant No. Guidarenjihezi [2014]14), Construction Program of Biology First-class Discipline in Guizhou (Grant No. GNYL[2017]009).” is deleted, and “The authors thank Shuchun Lin, the agronomist of the teaching and experiment farmer of Guizhou University, for his hard work in field experiment.” is added.

(17) Lines 698-710 in the revised version – “Supporting information” for S1 Table, S2 Table, S3 Table and S4 Table is added.

2. Response to Reviewer #1:

(1) However, the author didn`t perform a good combination between QTL and RNA-Seq data.

Reply: We accepted the reviewer’s comment and modified the sentence annotated on the original MS.

Lines 60-61 in the revised version – “Joint QTL mapping and RNA-sequencing analyses reveal one candidate gene of IIL1 (LOC106416451) for GSL metabolism in B. juncea.” is added in the “Abstract” for response to the joint analysis between QTL and RNA-Seq data.

Lines 380-391 in the revised version – “Joint QTL mapping and RNA-sequencing analyses reveal the candidate genes for GSL metabolism in B. juncea. To integrate the results of QTL mapping and RNA-sequencing, we perform an alignment analysis between the 24 DEGs related to GSL metabolism and the reference genome of B. juncea [16] by the BLAST-like alignment tool [54]. Only two DEGs of LOC106429668 encoding MYB28/MYB29/MYB76 (physical position: 23,048,306) and LOC106416451 (physical position: 5,833,626) encoding IIL1 were located on A08 of B. juncea genome. In addition, the DNA sequence designed for the PIP markers on A08 also blast the B. juncea reference genome [16]. The QTLs for 2-propenyl and 3-Butenyl GSL is located between the physical position of 18,549,777 (BnapPIP592) and the start point of the chromosome, the region of which was overlapped with the position of LOC106416451.” is added in the “Results” to perform a combination between QTL and RNA-Seq data.

Lines 452-459 in the revised version – “The joint QTL mapping and RNA-sequencing analyses reveal one candidate gene of LOC106416451 encoding IIL1. IIL1 is mainly responsible for the isomerization of 2-alkyl-malic acid to form 3-alkyl-malic acid [47,50,63,64]. In the present study, IIL1 is significantly highly (Log2 Fold Change=9.71, p=3.58E-15) expressed in the siliques of G302 with high aliphatic GSLs than that in G266 with low aliphatic GSLs. The primary work validates that the IIL1 might be the key gene for GSL regulation in the present RIL mapping population. However, more work is needed to narrow the QTL region and validate the candidate gene of LOC106416451 in our future study.” is added in the “Discussion” to discuss the combination between QTL and RNA-Seq data.

(2) Are there some DEGs location in the QTL regions, or all these DEGs are the downstream genes of QTL candidate gene?

Reply: We accepted the reviewer’s comment and modified the sentence annotated on the original MS.

Lines 380-391 in the revised version – the gene of LOC106416451 encoding IIL1 is located on the QTL region.

(3) A small question, DEseq2 uses read count to calculate DEG instead of FPKM.

Reply: We accepted the reviewer’s comment and modified the sentence annotated on the original MS.

Lines 350-356 in the revised version – “To identify differentially expressed genes (DEGs) involved in the GSL metabolism of siliques between G266 and G302. A rigorous algorithm with a threshold of “FDR≤0.05 and │Log2Ratio│≥1” was developed and used as thresholds to judge the significance of differences in transcript abundance.” replaced the original sentence to change the mistake.

3. Response to Reviewer #2:

(1) Polymorphism comparison between the parental lines, makers, genetic map can be simply introduced in introduction and results; part;

Reply: We accepted the reviewer’s comment and modified the sentence annotated on the original MS.

Lines 221-223 in the revised version– “These 306 polymorphic primers consisted of 130 primers from A. thaliana (42.5%), 143 primers from B. napus (19.2%) and 33 primers from B. rapa (13.6%).” was deleted for simplifying the results.

(2) The whole genome of B. juncea has been sequenced, how about the synteny relationships between the linkage map and sequencing results? It is better to show some comparative results because different B. juncea lines may have structural variations in certain regions on their genomes.

Reply: We accepted the reviewer’s comment and modified the sentence annotated on the original MS.

An excellent suggestion! We can focus on the structural variations of the Brassica juncea genome using one high-density map by SLAF technique in our future work. While we could only primarily use the map to do a primary QTL mapping for its limited number of ILP markers in the genetic map. In addition, we indeed detected the structural variation of the B. juncea genome, giving an example of the synteny analysis of LG08 with one published genetic map of B. juncea as shown in Lines 321-328.

(3) Transcriptome data analysis and processing can be briefly introduced, but with emphasis on describing transcriptome results and the differential expressed genes associated with those QTL results, especially those differential expressed genes involved into the 2-propenyl and 3-butenyl GSLs biosynthetic pathways;

Reply: We accepted the reviewer’s comment and modified the sentence annotated on the original MS.

Lines 60-61 in the revised version – “Joint QTL mapping and RNA-sequencing analyses reveal one candidate gene of IIL1 (LOC106416451) for GSL metabolism in B. juncea.” is added in the “Abstract” for response to the joint analysis between QTL and RNA-Seq data.

Line 340 and 342 in the revised version– “S2 Table” replaced “Table 4” for simplifying the results.

Line 358 in the revised version– “Table 4” replaced “Table 5”.

Lines 380-391 in the revised version – “Joint QTL mapping and RNA-sequencing analyses reveal the candidate genes for GSL metabolism in B. juncea. To integrate the results of QTL mapping and RNA-sequencing, we performs an alignment analysis between the 24 DEGs related to GSL metabolism and the reference genome of B. juncea [16] by the BLAST-like alignment tool [54]. Only two DEGs of LOC106429668 encoding MYB28/MYB29/MYB76 (physical position: 23,048,306) and LOC106416451 (physical position: 5,833,626) encoding IIL1 were located on A08 of B. juncea genome. In addition, the DNA sequence designed for the PIP markers on A08 also blast the B. juncea reference genome [16]. The QTLs for 2-propenyl and 3-Butenyl GSL is located between the physical position of 18,549,777 (BnapPIP592) and the start point of the chromosome, the region of which was overlapped with the position of LOC106416451.” is added in the “Results” to perform a combination between QTL and RNA-Seq data.

Lines 452-459 in the revised version – “The joint QTL mapping and RNA-sequencing analyses reveal one candidate gene of LOC106416451 encoding IIL1. IIL1 is mainly responsible for the isomerization of 2-alkyl-malic acid to form 3-alkyl-malic acid [47,50,63,64]. In the present study, IIL1 is significantly highly (Log2 Fold Change=9.71, p=3.58E-15) expressed in the siliques of G302 with high aliphatic GSLs than that in G266 with low aliphatic GSLs. The primary work validates that the IIL1 might be the key gene for GSL regulation in the present RIL mapping population. However, more work is needed to narrow the QTL region and validate the candidate gene of LOC106416451 in our future study.” is added in the “Discussion” to discuss the combination between QTL and RNA-Seq data.

(4) The discussion part may be focused on the significance and application of the new QTL detected for breeding. How about the relationship between the QTL on LG08 and previous published ones?

Reply: We accepted the reviewer’s comment and modified the sentence annotated on the original MS.

Lines 106-114 and 410-412 in the revised version–we have compared the QTL on LG08 with previous published papers.

Lines 430-436 in the revised version– “The novel QTL and the linked marker of At4g20150-1 can be helpful in exploiting the metabolic mechanism of 2-propenyl and 3-butenyl GSLs and molecular breeding in B. juncea.” was added for the “significance and application of the new QTL detected for breeding.”

Thank you in advance for kind attention!

Sincerely,

Entang Tian

Associate professor

Agricultural College of Guihzou University

Telephone: +86-851-3855894

Facsimile: +86-851-83621956

West Campus of Guizhou University

Guiyang City, China

550025

erictian121@163.com

Submitted filename: Response to the Reviewers.docx

Click here for additional data file.

7 Oct 2019

Exploring the basis of 2-propenyl and 3-butenyl glucosinolate synthesis by QTL mapping and RNA-sequencing in Brassica juncea

PONE-D-19-20230R1

Dear Dr. Tian,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

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PLOS ONE

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have answered all my questions and also made necessary revsion. I am satisfy with the revision

Reviewer #2: Authors has addressed all the corcerns raised by reviewers.

I am suggesting that this manuscript of the current version should be accepted for publishing.

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10 Oct 2019

PONE-D-19-20230R1

Exploring the basis of 2-propenyl and 3-butenyl glucosinolate synthesis by QTL mapping and RNA-sequencing in Brassica juncea

Dear Dr. Tian:

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