Open Access

An integrated analysis of QTL mapping and RNA sequencing provides further insights and promising candidates for pod number variation in rapeseed (Brassica napus L.)

Contributed equally
BMC Genomics201718:71

https://doi.org/10.1186/s12864-016-3402-y

Received: 27 April 2016

Accepted: 9 December 2016

Published: 11 January 2017

Abstract

Background

As the most important yield component in rapeseed (Brassica napus L.), pod number is determined by a series of successive growth and development processes. Pod number shows extensive variation in rapeseed natural germplasm, which is valuable for genetic improvement. However, the genetic and especially the molecular mechanism for this kind of variation are poorly understood. In this study, we conducted QTL mapping and RNA sequencing, respectively, using the BnaZNRIL population and its two parental cultivars Zhongshuang11 and No.73290 which showed significant difference in pod number, primarily due to the difference in floral organ number.

Result

A total of eight QTLs for pod number were identified using BnaZNRIL population with a high-density SNP linkage map, each was distributed on seven linkage groups and explained 5.8–11.9% of phenotypic variance. Then, they were integrated with those previously detected in BnaZNF2 population (deriving from same parents) and resulted in 15 consensus-QTLs. Of which, seven QTLs were identical to other studies, whereas the other eight should be novel.

RNA sequencing of the shoot apical meristem (SAM) at the formation stage of floral bud primordia identified 9135 genes that were differentially expressed between the two parents. Gene ontology (GO) analysis showed that the top two enriched groups were S-assimilation, providing an essential nutrient for the synthesis of diverse metabolites, and polyamine metabolism, serving as second messengers that play an essential role in flowering genes initiation. KEGG analysis showed that the top three overrepresented pathways were carbohydrate (707 genes), amino acid (390 genes) and lipid metabolisms (322 genes).

In silico mapping showed that 647 DEGs were located within the confidence intervals of 15 consensus QTLs. Based on annotations of Arabidopsis homologs corresponding to DEGs, nine genes related to meristem growth and development were considered as promising candidates for six QTLs.

Conclusion

In this study, we discovered the first repeatable major QTL for pod number in rapeseed. In addition, RNA sequencing was performed for SAM in rapeseed, which provides new insights into the determination of floral organ number. Furthermore, the integration of DEGs and QTLs identified promising candidates for further gene cloning and mechanism study.

Keywords

Brassica napus Pod number QTL mapping RNA sequencing DEG Candidate gene

Background

Pod number is one of the three yield components (pod number per plant, seed number per pod and seed weight) and breeding targets in rapeseed (Brassica napus L.). Among the three components of yield in rapeseed, pod number shows the highest correlation with yield, which suggests it to be the major contributor to yield [1]. Pod number, especially from branch inflorescence and whole plant, is also the most variable among the three yield components, in accordance with its modest heritability observed in most studies [2, 3]. In rapeseed germplasm resources, pod number shows extensive natural variation, which is invaluable for the genetic improvement [4]. However, the genetic and especially the molecular mechanism for this kind of variation are poorly understood.

Pod number is also a very complex trait that is multiplicatively determined by its three components: the number of flowers differentiated, the proportion of ovaries successfully fertilized, and the rate of fertilized ovaries developed into pods, which are determined by the flower bud differentiation, fertilization and pod development, respectively. Of these, flower bud differentiation is the first critical developmental stage and morphogenesis process which determines the number of floral organs [5]. This process is undertaken with the interaction of internal (such as carbohydrates, phytohormones, polyamine etc.) and external (such as light, temperature, water, and fertilizer etc.) factors [5]. Whereas, genetic factors are the most important control combined with internal signals (also genetically controlled) and influenced by environmental interactions. During the reproductive phase, the shoot apical meristem (SAM) produces inflorescence meristem (IM) that quickly develops into floral meristems (FMs) that, in turn, produce floral primordia [6]. In Arabidopsis, more than 100 regulators had been characterized to involve in SAM growth and development [610]. In addition, a complex regulatory network of hormones, transcription factors, enzymes, microRNA and other cellular components had been developed [11]. In rice, several genes involving in the regulation of floral organ number had been isolated, such as FON1-FON4 [1215]. However, molecular mechanism of floral bud differentiation remained relatively unclear in Brassica napus.

Pod number is a typical quantitative trait, which shows continuous variation and is very sensitive to the environmental conditions [16]. To the present, more than 80 pod number quantitative trait loci (QTLs) have been identified from nearly ten linkage mapping populations [4]. However, only a few of these QTLs were repeatedly detected, in accordance with the modest heritability of pod number. In addition, almost all of these pod number QTLs showed a moderate effect [4]. Therefore, it is difficult to narrow down these pod number QTLs and identify the underlying candidate genes.

The previous expression profiling studies largely relied on hybridization-based technologies, which was unable to fully catalogue and quantify the diverse RNA molecules that are expressed from genomes over a wide range of levels [17]. With the rapid advancement and decreased price of high-throughput sequencing technology in recent years, RNA sequencing has been widely applied in various species to conduct annotation and quantification of all genes and their isoforms across samples [18]. However, a large amount of differentially expressed genes (DEGs) were usually identified between contrast samples through RNA sequencing. Therefore, integration of DEGs and QTLs is considered to be a promising method to identify potential candidate genes [19].

In our previous study, we screened two rapeseed cultivars Zhongshuang11 and No.73290 that showed significant difference in pod number [4] mainly due to the difference in floral organ number. The main objectives of our study are to: (1) dissect the genetic mechanism of pod number variation by QTL mapping using BnaZNRIL and BnaZNF2 population derived from Zhongshuang11 and No.73290; (2) dissect the molecular mechanism of pod number variation by characterizing the expression profile and DEGs of SAM for Zhongshuang11 and No.73290 using RNA sequencing technology; (3) integrate the QTLs and DEGs to identify potential candidate genes for further functional and mechanism studies.

Results

Mapping of QTLs for pod number using BnaZNRIL population

The pod number of No.73290 was much larger than that of Zhongshuang11 in all of the three investigated environments (Table 1). The pod number of the BnaZNRIL population showed normal or near-normal distribution in the four environments (Fig. 1), indicating a quantitative inheritance suitable for QTL mapping [4]. The broad-sense heritability was 0.67, 0.62 and 0.62 for pod number of main inflorescence, branch inflorescence and whole plant, respectively (Additional file 1: Table S1).
Table 1

Pod number for the two parents and derived RIL populations in the four investigated environments

Environment

Materials

PNm

PNb

PNw

W13

Parents

Zhongshuang11

71

130

201

No.73290

107

211

306

Pt-test

3.82E-05

4.98E-03

4.41E-04

RIL

Min

62

39

115

Max

136

356

458

Mean

92

165

257

Z13

Parents

Zhongshuang11

60

104

163

No.73290

87

230

322

Pt-test

5.56E-05

9.83E-03

2.96E-03

RIL

Min

22

58

88

Max

99

480

566

Mean

68

183

246

W14

Parents

Zhongshuang11

78

101

179

No.73290

108

146

237

Pt-test

5.42E-07

3.96E-03

4.88E-03

RIL

Min

45

45

126

Max

121

362

419

Mean

87

170

255

Z14

Parents

Zhongshuang11

66

102

178

No.73290

/

/

/

Pt-test

/

/

/

RIL

Min

33

28

75

Max

129

390

491

Mean

74

143

216

Total

Parents

Zhongshuang11

71

110

184

No.73290

100

192

280

Pt-test

1.38E-12

2.72E-07

3.81E-08

Population

Min

22

28

75

Max

136

480

566

Mean

81

164

243

PNm/PNb/PNw was the abbreviation of pod number from the main inflorescence, branch inflorescence and whole plant. W13, Z13, W14 and Z14 were the codes of the four environments

Fig. 1

Distribution of pod number in the BnaZNRIL population in the four environments. The horizontal axis represented the value of pod number for the main inflorescence (a), branch inflorescence (b), and whole plant (c). The vertical axis represented the number of lines within the BnaZNRIL population. The different experiments were represented by different colors as showed in the legend. W13RIL, Z13RIL, W14RIL and Z14RIL were the codes of the four experiments

A high-density linkage map comprising 2264 SNP markers was constructed using the BnaZNRIL population. After deleting two non-reproducible suggestive QTL, a total of eight QTLs were identified for pod number in four experiments, six and two for main inflorescence (PNm) and branch inflorescence (PNb), respectively (Fig. 2). The identified QTLs were distributed on A01 (qPN.A01-2, −3,), A02 (qPN.A02-1), A03 (qPN.A03-3), A04 (qPN.A04-1), A06 (cqPN.A06-1), C04 (qPN.C04-1) and C06 (qPN.C06-2) linkage groups. These QTLs for PNb and PNm explained 9.3–9.9% and 5.8–11.9% of phenotypic variance, respectively. The additive effect of these QTLs for PNb and PNm ranged from −16.3 to −13.8 and from −4.1 to 2.5, respectively. Furthermore, the corresponding genomic intervals of the eight QTLs were determined through mapping the probe sequences of SNP markers within their confidence intervals to the Brassica napus genome (Additional file 2: Table S2).
Fig. 2

QTL scanning curves for pod number in the BnaZNRIL population. The horizontal axis represented the no. of the 19 linkage groups (A1-A10; C1-C9). The vertical axis represented the LOD score. PNm/PNb/PNw was the abbreviation of pod number from the main inflorescence, branch inflorescence and whole plant. W13, Z13, W14 and Z14 were the codes of the four experiments. The straight lines on the middle of the figure indicated the LOD threshold values corresponding to P = 0.1

Because QTL mapping of pod number had been previously performed using the BnaZNF2 population derived from the same parents [4], meta-analysis was carried out to integrate pod number QTLs detected in the two studies. After meta-analysis, a total of 15 consensus-QTLs were obtained (Table 2), of which seven were identical to the previously identified ones, whereas the other eight should be novel (Additional file 3: Table S3).
Table 2

The 15 consensus- QTLs of pod number obtained after meta-analysis from BnaZNF2 and BnaZNRIL population

QTL

Peak position

Confidence interval

Genomic region(Mb)

Additive effect

LOD value

R 2 (%)

Experiments code

qPN.A01-2

74.9

61.9–85.8

4.4–8.7

−16.3

4.3

9.3

W13RILb

qPN.A01-3

98.4

87.2–103.9

8.7–15.7

−13.8

4.9

9.9

W14RILb

qPN.A01-1

104.4

94.3–105.8

15.3–18.3

−4.9

3.0

12.5

W10F2:3m/X11F2:3m

qPN.A02-1

4.2

1.9–5.3

0.1–0.4

−4.1

6.5

11.2

W13RILm

qPN.A03-1

61.5

57.5–61.9

11.4–13.5

−9.7

3.6

3.0

W11F2:3w

qPN.A03-2

81.6

64.6–98.0

14.2–15.4

−1.7

3.8

3.4

W11F2:3m

qPN.A03-3

142.7

130.5–155.5

15.5–20.8

−3.6

3.9

10.5

Z13RILm

qPN.A04-1

41.9

37.0–51.3

11.5–13.3

−3.3

6.0

10.2

W13RILm

qPN.A05-1

10.8

4.0–13.8

6.0–8.0

−4.7

3.4

12.6

W09F2m

cqPN.A06-1

/

/

22.0–23.7

−5.6

7.9

20.6

W10F2:3bmw/W11F2:3bmw/X11F2:3bmw/X11F2:4bmw/W14RILm

qPN.A09-1

44.0

42.5–54.4

17.6–24.0

4.1

2.7

2.3

X11F2:3bw

qPN.C02-1

8.2

3.2–21.5

1.3–2.5

−5.1

4.3

18.5

W10F2:3m/W11F2:3m/X11F2:3m

qPN.C04-1

128.5

119.2–129.6

36.7–45.7

−3.3

4.2

8.7

Z13RILm

qPN.C06-1

51.2

49.1–53.0

17.9–18.8

7.3

4.4

15.0

W10F2:3m/W11F2:3m

qPN.C06-2

20.2

20.1–20.3

29.3–31.3

2.5

3.5

5.8

W13RILm

W13, Z13, W14 and Z14 were the codes of the four environments

Because the flowering time of the two parents (Zhongshuang11 and No.73290) for both BnaZNF2 and BnaZNRIL populations differed for about one week, therefore the 15 pod number consensus-QTLs were also compared with the flowering time QTLs detected in both populations. The results showed that most of the QTLs for both traits were not overlapped (Shi et al. unpublished data).

Based on rapeseed genome annotation [20], a total of 6164 genes resided in regions of 15 QTLs. Number of rapeseed genes in QTL region ranged from 63 (qPN.A02-1) to 1103 (qPN.C04-1), with the mean of 411 (Table 3). The majority of these genes have never been functionally annotated in Brassica napus.
Table 3

Number of genes, DEGs and meristem-related genes within the QTL region

QTL

Gene

DEG

Total

Related to meristem development

qPN.A01-2

781

74

0

qPN.A01-3

675

74

1

qPN.A01-1

338

43

0

qPN.A02-1

63

11

0

qPN.A03-1

375

40

1

qPN.A03-2

288

33

2

qPN.A03-3

939

51

0

qPN.A04-1

280

34

0

qPN.A05-1

248

23

1

cqPN.A06-1

289

43

0

qPN.A09-1

223

33

0

qPN.C02-1

212

62

2

qPN.C04-1

1103

69

0

qPN.C06-1

82

15

0

qPN.C06-2

268

42

2

Gene represented total rapeseed genes. DEGs represented the differently expressed genes between Zhongshuang11 and No.73290. Homologous represented meristem-related genes

Transcriptome sequencing and mapping

To dissect the possible molecular mechanism of pod number difference between Zhongshuang11 and No.73290, transcriptome sequencing was performed using the shoot apical meristem (SAM) of Zhongshuang11 and No.73290 because their pod number difference was mainly attributable to the floral organ number. Total RNA, isolated from SAM of ten individuals of Zhongshuang11 and No.73290 at the formation stage of floral bud primordia, was pooled and transcribed into cDNA. Then, the two cDNA libraries were constructed and sequenced via Illumina Hiseq 2000 platform. Consequently, 172,006,032 and 170,055,032 raw reads were generated for Zhongshuang11 and No.73290, respectively. After removal of sequences with adaptor, duplication and low quality, we obtained 157,471,012 and 155,457,898 high-quality clean reads for Zhongshuang11 and No.73290, respectively. A summary of the transcriptome sequencing is exhibited in Table 4.
Table 4

Summary of the transcriptome sequencing

Sample name

Raw reads

Clean reads

Cleans bases

Error rate (%)

Q20 (%)

Q30 (%)

GC content (%)

Zhongshuang11

172,006,032

157,471,012

15,408,439,159

0.03

99.45

95.42

46.03

No.73290

170,055,032

155,457,898

15,207,889,681

0.03

99.45

95.39

46.13

A total of 127,275,824 (80.82%), and 125,875,780 (80.97%) clean reads for Zhongshuang11 and No.73290, respectively, were successfully mapped to the released reference genome [20] of Brassica napus using SOAPaligner/soap2 [21] (Table 5). Of which, 106,578,237 (67.68%) and 105,439,605 (67.83%) clean reads for Zhongshuang11 and No.73290, respectively, were uniquely mapped to the reference genome.
Table 5

The number and proportion of (uniquely) mapped reads among clean reads

Sample name

Mapped reads (Mapped/Clean)

Uniquely mapped reads (Unique/Clean)

Zhongshuang11

127,275,824 (80.82%)

106,578,237 (67.68%)

No.73290

125,875,780 (80.97%)

105,439,605 (67.83%)

Identification and validation of DEGs between Zhongshuang11 and No.73290

All the uniquely mapped reads were selected for further analysis. To remove technical biases inherent in the sequencing process, most notably the length of the RNA species and the sequencing depth of samples, RPKM (reads per kilobase per million reads) was used to normalize these measures [22]. A total of 68030 (67.33%) and 69733 (69.02%) genes expression were detected for Zhongshuang11 and No.73290 respectively. Notably, 64440 (87.89%) genes expression was commonly detected in both Zhongshuang11 and No.73290 (Fig. 3a). Genes with average RPKMs beyond 60 were selected to conduct gene ontology (GO) analysis (Fig. 3b). These genes involved in biological processes including electron transport or energy pathway, protein metabolism and other biological process. The most enriched category in molecular function was structural molecule activity. The most overrepresented category in cellular component were ribosome, followed by cytosol, other cellular components and cell wall.
Fig. 3

Characteristics of expression genes in SAM of Zhongshuang11 and No.73290. a The Venn diagram showing the overlaps of expression genes between Zhongshuang11 and No.73290. b GO analysis of expression genes with average RPKMs beyond 60 using the Classification SuperViewer Tool (http://bar.utoronto.ca/ntools/cgi-bin/ntools_classification_superviewer.cgi). Groups with gray words indicated unenriched GO terms (p ≥ 0.05)

Using false discovery rate (FDR) < 0.001 and fold change > 2 as the significant level, a total of 9135 differentially expressed genes (DEGs) were identified. Of which, 5507 (60.28%) and 3628 (39.72%) DEGs were respectively up and down regulated, in No.73290 compared to Zhongshuang11 (Additional file 4: Table S4).

To validate the results of RNA sequencing, qRT-PCR was conducted on 12 randomly selected DEGs with 7 down-regulated and 5 up-regulated genes. As shown in Fig. 4, the expression patterns for 12 DEGs were generally consistent between RNA sequencing and qRT-PCR data. Whereas, differences in fold changes measured by RNA sequencing and qRT-PCR were observed, which is consistent with other studies [23].
Fig. 4

Comparison of the relative expression abundance measured by qRT-PCR and RNA-seq for 12 selected DEGs

Functional annotation of the DEGs

All the DEGs were annotated based on their similarity (E value ≤1e-5) in six public databases, including GO, KEGG, Pfam, InterPro, SwissProt, and TrEMBLE (Table 6). In total, 8479 DEGs (92.82%) have significant similarity in at least one database.
Table 6

Summary of functional annotation for total DEGs in six public databases

 

No. of DEGs hits

Percentage (%)

Annotated in GO

5433

59.47

Annotated in KEGG

4576

50.09

Annotated in Pfam

7300

79.91

Annotated in InterPro

7082

77.53

Annotated in Swiss-Prot

6307

69.04

Annotated in Tremble

8433

92.32

Annotated in at least one database

8479

92.82

Total DEGs

9135

100

Because the majority of the rapeseed reference genes have no functional annotation. These DEGs were then annotated by sequence alignment with those in TAIR 10 (E-value cutoff of 1e-5). Finally, 8305 (90.91%) of 9135 DEGs matched at least one gene in Arabidopsis (Additional file 5: Table S5). To overview functions of DEGs, the 8305 annotated DEGs were assigned to at least one Gene Ontology (GO) category that belonged to three major terms: cellular component, molecular function and biological process. After the absolute gene numbers in each group were normalized to the frequency of group over all Arabidopsis genes (Fig. 5), S-assimilation was the most over-represented group, indicating that the role of S-assimilation in SAM growth and development. In plant, the uptake of sulfate and its assimilation provides an essential nutrient for the synthesis of diverse metabolites, including cystesine, methionine, glutathione, vitamin cofactors and so on [24]. These metabolites affect carbon nitrogen ratio, which likely contributes to floral bud development [25]. The following over-represented group was polyamine metabolism. Previous studies indicated that polyamine served as second messengers playing an essential role in flowering genes initiation [26]. Other significantly enriched groups, such as N-metabolism, amino acid metabolism, lipid metabolism or hormone metabolism, displayed the active metabolic status of SAM.
Fig. 5

Gene functional classification of differentially expressed genes using the Classification SuperViewer Tool (http://bar.utoronto.ca/ntools/cgi-bin/ntools_classification_superviewer.cgi). The X axis shows the normalized class score (± bootstrap StdDev). Groups with gray words indicated unenriched GO terms (p ≥ 0.05)

To further study the biological pathways that related to SAM growth and development, the pathway enrichment analysis was conducted in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database. These DEGs were mapped to 190 KEGG pathways (Additional file 6: Table S6), which were grouped into five categories: cellular processes, genetic information processing, environmental information processing, metabolism, and organismal systems (Fig. 6). Of which, metabolism (2835 genes, 72.01%) was most enriched, which indicated that developmental differences of SAM between Zhongshuang11 and No.73290 were largely related to metabolism. Moreover, the top three represented pathways were carbohydrate metabolism (707 genes), amino acid metabolism (390 genes) and lipid metabolism (322 genes), which all belonged to the metabolism group. This is understandable, because the flower bud differentiation is dependent first on the nutrient level in the body of plant, which is reflected by the cytosol concentration (in the shoot apex growing point) that is determined by the metabolic process. Carbohydrate (as the structure and energy matter) accumulation is closely related to flower bud differentiation [5]. Increasing amino acid content promotes flower bud formation [27]. Lipid metabolism contributes to cell membrane and other parts. Because the SAM is a reservoir of undifferentiated stem cells that function as a continuous source of new cells [11]. These results provide further insight into the molecular mechanism responsible for floral organ number variation in rapeseed.
Fig. 6

KEGG classification of the DEGs. Total pathways were grouped into five categories: cellular process, environmental information process, genetic information process, metabolism and organismal systems. Figures indicated the number of DEGs

Integration of DEGs with QTLs

To further understand the roles of these DEGs played in regulating SAM development and the final flower/pod number, they were integrated with the total of 15 pod number consensus-QTLs identified in both BnaZNRIL and BnaZNF2 population by in silico mapping. A total of 647 DEGs were located in the regions of the 15 QTLs, and the DEGs number within each QTL ranged from 11 (qPN.A02-1) to 74 (qPN.A01-2/ qPN.A01-3), with a mean of 43 (Table 3). Of which, 604 DEGs (93.35%) showed significant homology with Arabidopsis genes and the most have functional annotation.

Interestingly, nine DEGs underlying six QTLs were known to play an important role in regulating meristem growth and development. The nine DEGs resided in the regions of qPN.A01-3(BnaA01g22100D), qPN.A03-1(BnaA03g25890D), qPN.A03-2(BnaA03g29180D, BnaA03g29810D), qPN.A05-1(BnaA05g12220D), qPN.C02-1(BnaC02g02900D, BnaC02g03640D), and qPN.C06-2(BnaC06g28880D, BnaC06g29980D) (Table 7). Their functions included transcription factor, auxin receptor, enzyme, and other cellular elements. For example, BnaA03g25890D (underlying qPN.A03-1) is homologous to Arabidopsis ABP1 (AT4G02980), which is an extra-cellar auxin receptor that involves in the maintenance of anisotropic growth to initiate new floral primordial [28]. BnaC06g29980D (underlying qPN.C06-2) is homologous to Arabidopsis AP1 (AT1G69120), a floral homeotic gene encoding MADS-domain transcription factor, which plays an essential role in floral meristem determinacy, maintenance of floral meristem identity, flower development, and the transcriptional activation of several flowering time genes including AG, SVP, SOC1 and AGL24 [29, 30]. BnaC02g02900D (underlying qPN.C02-1) is homologous to Arabidopsis TFL1 (AT5G03840), which is involved in floral initiation process and control floral meristem identity [31]. The nine genes should be considered as promising candidates that could be used for further study. Our study also indicated that integration of transcriptome sequencing with QTL mapping could be an efficient method to narrow down the number of candidate genes within the QTL region.
Table 7

Details of nine DEGs those are related to meristem development and within the QTL regions

QTL

Rapeseed gene

Arabidopsis gene

Gene function

qPN.A01-3

BnaA01g22100D

AT1G60340

Transcriptional regulator

qPN.A03-1

BnaA03g25890D

AT4G02980

Auxin binding protein

qPN.A03-2

BnaA03g29180D

AT3G05840

Kinase

BnaA03g29810D

AT3G07050

Inflorescence meristem maintenance

qPN.A05-1

BnaA05g12220D

AT2G30130

Repress KNOX gene expression

qPN.C02-1

BnaC02g02900D

AT5G03840

Inflorescence meristem identity

BnaC02g03640D

AT5G02030

Transcription factor

qPN.C06-2

BnaC06g28880D

AT1G67770

Mei2-like proteins

BnaC06g29980D

AT1G69120

Transcription factor

Discussion

Novel QTLs identified for pod number

In the present study, a total of eight QTLs for pod number were identified in the BnaZNRIL population, and were integrated with those previously detected in BnaZNF2 population [4], which resulted in a total of 15 consensus-QTLs. These QTLs were distributed on ten linkage groups (A01, A02, A03, A04, A05, A06, A09, C02, C04 and C06). Most of these QTLs showed a moderate effect (R 2 < 10%) and only one (cqPN.A06-1) can be considered as a “major” QTL. Of these, only four QTLs were repeatedly detected and the other 11 were specific (Table 2). This strongly indicated that pod number is very sensitive to environmental condition, and accorded well with the moderate heritability of pod number found in the current and previous studies [2, 3].

More than 70 QTLs for pod number had been detected in the other studies, which were distributed on 17 (excluding C04 and C07) of the 19 linkage groups [4]. To accurately identify the positional relationship of pod number QTLs detected in our and other studies, comparative QTL analysis was performed based on the physical map of Brassica napus [4]. Of the total of 89 pod number QTLs identified in our and other studies, 83 ones were successfully mapped on the physical map. This represents the first relatively comprehensive genetic architecture of pod number in rapeseed. Of the 15 consensus-QTLs detected in our studies, the genomic regions of seven QTLs (qPN.A01-2, qPN.A02-1, qPN.A03-1, qPN.A03-2, qPN.A03-3, qPN.C06-1 and qPN.C06-2) were overlapped with those reported in other studies, whereas the other eight (qPN.A01-3, cqPN.A01-1, qPN.A04-1, qPN.A05-1, cqPN.A06-1, qPN.A09-1, qPN.C02-1 and qPN.C04-1) should be novel. More importantly, we detected a pod number QTL (qPN.C04-1) on C04 linkage group for the first time.

Characteristics of SAM DEGs

Transcriptome sequencing of SAM had been documented in several crops such as maize [32] and soybean [33], but had rarely been reported in Brassica napus. To our knowledge, this is the second report on SAM transcriptome sequencing in rapeseed. In this study, KEGG analysis showed that the most enriched group of these DEGs belong to metabolism, especially carbohydrate metabolism, which provides the energy and material basis to produce the difference of the number of differentiated flower buds and opening flowers. We proposed that these carbohydrates should be mostly transferred from the leaf, because it is the major source of photosynthesis during the vegetative and early reproductive stage.

To date, a total of 2296 transcription factors have been identified and classified into 58 families in Arabidopsis (http://planttfdb.cbi.pku.edu.cn/index.php?sp=Ath). Based on the functional annotation of corresponding Arabidopsis homologous of these DEGs, nearly all transcription factors families (52 of the total 58 families) were found. This indicated that transcription factors may play an important role in SAM development. In addition, phytohormones metabolism was overrepresented. The roles of several types of hormones in SAM development have been extensively studied. Of which, cytokinins and auxins act as two major hormones to involve in meristem function and maintenance [34]. Auxins are a positive regulator to induce lateral organ initiation, such as flower bud, leaf. Cytokinins play a role in meristem maintenance and in controlling meristematic properties, such as cell proliferation. Furthermore, auxins interact with cytokinins to regulate SAM development [34, 35]. In the SAM, gibberellin activity is down-regulated and its activity is antagonistic with cytokinins activity [35]. In the SAM, brassinosteroids play a role in the spatiotemporal control of organ boundary formation and morphogenesis [36]. What’s more, phytohormones and transcription factors can cooperate to balance meristem maintenance and organ production [35]. These results showed a complex regulatory network involved in SAM development.

Candidates for pod number

Previous studies into pod number mainly focused on QTL mapping (in genomics level) and no study has been conducted in transcriptomics. RNA sequencing is a new high-throughput, high-sensitivity and high-speed technology, which is widely used for transcriptome profiling [37]. In comparison with the previous methods, RNA sequencing possesses three key advantages: first, RNA sequencing is not limited to detect transcripts that correspond to existing genomic sequences; Second, RNA sequencing has very low background signal; Third, transcripts detected by RNA sequencing have a large dynamic range of expression levels [38]. The integration of the transcriptome profiling and QTL mapping is a very useful and popular strategy toward discovery of candidate genes [19].

Our previous study showed that pod number of No.73290 was about half greater than that of Zhongshuang11 [4], which was mainly due to their difference in floral organ number according to results of successive observation in several years. According to the relevant research, floral organ number was mainly determined by the SAM development [12, 39]. Thus, SAM of both Zhongshuang11 and No.73290 was characterized using the RNA sequencing technology and the identified DEGs was combined with QTLs to narrow the candidates.

According to the functional annotation of the corresponding Arabidopsis homologues of these DEGs, nine involved in meristem growth and development were considered to be the candidates, which should be important targets for further functional validation.

Transcription factors (such as WUS, STM) play a central role in SAM growth and development [11, 40]. Of the above-mentioned nine candidates, the Arabidopsis homologues (AP1, BLH9, and AT1G60340) of BnaC06g29980D, BnaC02g03640D and BnaA01g22100D were transcription factor-encoding genes. AP1, a MAD box transcription factor, activates floral organ identity genes to promote FMs formation by interplaying with LFY [29, 41, 42]. Loss function of AP1 prevents the formation of flowers in the axils of sepal to form FMs [43]. In our study, the expression level of BnaC06g29980D was higher in No.73290 than in Zhongshuang11, which might promote flower formation. BLH9, a homeodomain transcription factor, contributes to spatial expression patterns of boundary genes and floral induction by FT [44]. Loss function of this gene impairs stem cell maintenance and blocks internode elongation and flowering [45]. AT1G60340 belongs to the NAC family, whose proteins have a consensus sequence known as the NAC domain (petunia NAM and Arabidopsis ATAF1, ATAF2, and CUC2) [46]. Previous studies showed that mutated NAM (NO APICAL MERISTEM) genes failed to form SAM in petunia and that mutated CUC2 causes defect in the formation of the SAM in Arabidopsis [46].

Phytohormones, such as auxin, cytokinins and gibberellin etc., play an important role in the shoot apical and floral meristem functions [35]. Of the above-mentioned nine candidates, BnaA03g25890D is homologous to Arabidopsis ABP1, which functions as an auxin receptor and its knockdown leads to an enhanced degradation of AUX/IAA repressors [47]. In current study, the expression level of BnaA03g25890D was lower in No.73290 than in Zhongshuang11, which might promote flower primordium initiation.

BnaA03g29180D is homologous to ATSK12, which encodes SHAGGY-like protein kinase involved in meristem organization [48]. BnaA05g12220D is homologous to ASL5, which encodes the protein containing a conserved LOB domain. The T-DNA insertion mutant of ASL5 displayed lost apical dominance, abnormal inflorescence compare to wide type [49, 50]. In our study, the expression level of BnaA03g29180D and BnaA05g12220D in No.73290 was higher more than in Zhonghsuang11, which suggested that BnaA03g29180D and BnaA05g12220D may positively regulate the SAM development.

BnaC02g02900D is homologous to TFL1 (encoding a small transcription cofactors), which controls inflorescence meristem identity and is involved in the floral initiation process [31, 51]. To date, the function of TFL1 has been extensively studied in soybean [52], rose [53] and black cherry [54] etc., which all indicated its role in the transcription repression in floral development. In our study, the expression level of BnaC02g02900D was lower in No.73290 than in Zhongshuang11, which might promote floral development. BnaA03g29810D is homologous to NSN1, which encodes a nucleolar GTP-binding protein and is required for maintenance of inflorescence meristem identity and floral organ development.[55]. Loss function mutant of NSN1 showed a smaller inflorescence meristem dome in comparison to the wide type plants at young stages [56]. In current study, BnaA03g29810D showed higher expression level in No.73290 than in Zhongshuang11, which might promote floral organ development. BnaC06g28880D is homologous to TEL2, which is similar to the terminal ear1 (TE1) gene in maize. The te1 mutant displayed earlike appearance of the terminal inflorescence, which indicated that TEL2 may contribute to normal inflorescence and lead to floral development [57]. The expression level of BnaC06g28880D was higher in No.73290 than in Zhongshuang11, which might promote inflorescence and flower development.

To further validate the nine candidate genes, their over-expression and knockout vector have been constructed and transformed in our laboratory.

Conclusions

To investigate the genetic and molecular mechanism of pod number variation in rapeseed, we conducted QTL mapping and RNA sequencing, respectively, using the BnaZNRIL/BnaZNF2 populations and its two parents Zhongshuang11 and No.73290. As a result, 15 consensus-QTLs were identified through meta-analysis. Go and KEGG analysis of 9135 DEGs between the SAM of Zhongshuang11 and No.73290 provided new and further insights into pod number variation. An integrated analysis of QTLs and DEGs identified several promising candidates for these QTLs, which laid a solid foundation for further functional and mechanism research.

Methods

Population construction, field experiments and trait investigation

Two linkage populations, BnaZNF2 and BnaZNRIL, were used for mapping and integration of QTLs for pod number in our study. Both populations were derived from the two sequenced rapeseed cultivars, Zhongshuang11 (de novo sequencing) and No.73290 (re-sequencing) that showed significant difference on pod number. The BnaZNF2 population included 184 F2 individuals, which had been reported previously [4]. The BnaZNRIL population included 184 F7 generation RILs (recombinant inbred lines) derived from the above-mentioned 184 F2 individuals using single-seed descent.

The BnaZNRIL population was planted in Wuhan (Hu Bei province, China) and Zhengzhou (He Nan province, China) from Oct. 2012 to May 2013 and from Oct. 2013 to May 2014 (code W13RIL, Z13RIL, W14RIL, and Z13RIL, respectively) followed a randomized complete block design with two replications. Each block contained three rows, with 33 cm distance between rows and 16.7 cm distance between individuals. The field management followed standard agriculture practice. At maturity, 10 representative plants from the middle of the second row of each block were harvested.

Effective pod number was investigated according to the previous study [3]. Pod number included three categories: pods from the main inflorescence (PNm), branch inflorescence (PNb) and whole plant (PNw), respectively.

SNP genotyping and linkage map construction

To genotype the BnaZNRIL population, The Brassica 60 K Illumina® Infinium SNP array was recently developed by the international Brassica Illumina SNP consortium. The array hybridization and data processing were carried out in Emei Tongde Co. (Beijing) according to the manufacturer’s recommendations. Leaf tissue from seedlings of BnaZNRIL population was used to extract genomic DNA according to the CTAB method [58].

The genetic linkage map was constructed using the software JoinMap 4.1 (https://www.kyazma.nl/index.php/JoinMap/) with a threshold for goodness-of-fit ≤5, recombination frequency of < 0.4 and minimum logarithm of odds (LOD) score of 2.0. The genetic distances were measured based on the Kosambi function. To avoid the potential errors, double-crossover events were checked.

QTL mapping and meta-analysis

QTL mapping was performed using the composite interval mapping (CIM) method incorporated into WinQTLCart v2.5 software (http://statgen.ncsu.edu/qtlcart/WQTLCart.htm). The parameters including walk speed, number of control markers, window size and regression method were set to 1 cM, 5, 10 cM, and forward regression, respectively. Permutation analysis [4] with 1000 repetitions was used to determine the LOD threshold. The LOD value corresponding to P = 0.05 (3.0–4.6) was used to detected significant QTLs. Moreover, a lower LOD value corresponding to P = 0.10 (2.6–4.1) was employed to identify suggestive QTLs with small effects.

Meta-analysis was used to estimate the number and position of the true QTLs underlying the QTLs of the same or related traits, which were repeatedly detected in the different environments and/or populations [59]. QTLs repeatedly detected in the same population from different environments (year-location combinations) were first integrated into identified QTLs, and then QTLs repeatedly detected in the different populations were integrated into consensus-QTLs. Each identified and consensus-QTLs were named according to a previous study [60].

SAM sampling and RNA isolation

To gain credible data, the SAMs were sampled from 10 representative individuals growing at the formation stage of floral bud primordia and then equally mixed for Zhongshuang11 and No.73290, respectively. Total RNA was isolated using an RNeasy Plant Mini Kit (Cat. 74124, Qiangen, Mississauga, ON) according to the manufacturer’s protocol. Then trace amount of DNA was treated with DNase I (Cat. 18068–015, Invitrogen, USA). In addition, the RNA quantity and quality were detected using a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific, USA), and the integrity of RNA was detected by electrophoresis with a 1% agarose gel in 1 ╳ TBE buffer at 70 V for 45 min.

cDNA libraries construction and Illumina sequencing

Sequencing libraries were prepared using NEB Next Ultra directional RNA Library Prep Kit for Illumina (San Diego, CA, USA) according to the manufacturer’s recommendations. Briefly, purification of mRNA was achieved from total RNA by using ploy-T oligo-attached magnetic beads. Then, adding the Illumina proprietary fragmentation buffer to the RNA samples, fragmentation of mRNA was completed under elevated temperature. Taking those fragments as templates, the first-strand cDNA was synthesized via using random hexamers and SuperScript II. The second-strand cDNA was synthesized by adding buffer, dNTPs, DNA polymerase I and RNase H to the reaction system. Subsequently, purification of the double-strand cDNA was performed using AMPure XP beads. The remaining overhangs were repaired via exonuclease/polymerase. Adenylation of the 3’ ends of cDNA fragments were conducted. Illumina PE adapter oligonucleotides were ligated to prepare for hybridization. 200-bp cDNA fragments were extracted using an AMPure XP system (Beckman Coulter, Beverly, CA, USA). DNA fragments were amplified by 12 cycles of PCR. After enrichment of DNA, the libraries were sequenced using Illumina Hiseq 2000 platform and raw data was generated. Adaptor, duplication and low quality sequences were removed to get clean data.

Differential gene expression analysis

First, all reads of each library were mapped to the reference genome using the SOAP program [21] with the default parameters. Second, the uniquely mapped reads were selected for quantifying the abundance. Third, the expression level was normalized using the values of RPKM (reads per kilobase per million reads). According to Bioconductor [61] package, DEseq (http://www.bioconductor.org/packages/release/bioc/html/DESeq.html) was used to measure gene differential expression between Zhongshuang11 and No.73290. The absolute value of log2 (Ratio) ≥ 1 (under the criterion of P ≤ 0.01 and false discovery rate (FDR) ≤ 0.001) was used as threshold to assess the significance of gene expression difference.

Functional annotation and category

Total DEGs were annotated based on BLSAT search in six public databases, including the protein family (Pfam) database (http://pfam.xfam.org/), InterPro database (http://www.ebi.ac.uk/interpro/entry/IPR001461), Swiss-Prot protein database (http://web.expasy.org/docs/swiss-prot_guideline.html), TrEMBLE database (http://web.expasy.org/docs/swiss-prot_guideline.html), the gene ontology (GO) database (http://geneontology.org/) and the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) database (http://www.genome.jp/kegg/pathway.html).

DEGs were functionally annotated using BLASTN with an E value cutoff of 1.0E-5 in TAIR 10 database. Then, The GO classification was performed using Classification SuperViewer Tool (http://bar.utoronto.ca/ntools/cgi-bin/ntools_classification_superviewer.cgi).

Pathway analysis was performed by searching against the Kyoto Encyclopedia of Gene and Genome (KEGG) pathway database with E value threshold of 1.0E-5.

Validation of RNA sequencing data by qRT-PCR

Twelve genes were selected randomly to validate RNA sequencing data by quantitative real-time PCR (qRT-PCR). The primer pairs were designed using Primer 5.0. Details of primer pairs were listed in Additional file 7: Table S7. Total RNA extracted for transcriptome sequencing was used for conducting qRT-PCR. qRT-PCR was performed using SYBR® Select Master Mix (2X) according to manufacturers’ recommendation. UBC21 gene was used as an internal control to normalize transcript levels [62]. Real-time assay for each gene was performed with three independent biological replicates under identical conditions. Gene expression was calculated according to the previous study [63].

Abbreviations

DEG: 

Differentially expressed gene

FDR: 

False discovery rate

FM: 

Floral meristem

GO: 

Gene ontology

IM: 

Inflorescence meristem

KEGG: 

Kyoto encyclopedia of genes and genomes

QTL: 

Quantitative trait loci

RIL: 

Recombinant inbred line

RPKM: 

Reads per kilobase per million reads

SAM: 

Shoot apical meristem

SNP: 

Single nucleotide polymorphism

Declarations

Acknowledgements

Part of the filed work was carried out at the farm of Henan Academy of Agricultural Sciences. The authors thank Professor Shufen Zhang, Dr. Junping He and Mrs Yujiao Liu for their kind help in field experiment and management.

Funding

This research was supported by the National Basic Research and Development Program (2015CB150202), the Rapeseed Industry Technology System (CARS-13), the Agricultural Science and Technology Innovation Project (CAAS-ASTIP-2013-OCRI), the Core Research Budget of the Non-profit Governmental Research Institution (1610172014001), and the Hubei Agricultural Science and Technology Innovation Center of China.

Availability of data and materials

The datasets supporting the conclusions of this article are available in the Sequence Read Archive at the National Center for Biotechnology Information (NCBI) at the accession number SRX1715587. The datasets supporting the conclusions of this article are included within the article and its additional files.

Authors’ contributions

JS and HW designed the experiments; JS performed the construction, genotyping and phenotyping of the RIL population; YY collected samples and prepared RNA. JY performed QTL scanning and expression analysis. XW and GL performed the field experiments; JY and JS conducted the data analysis and wrote the manuscript. All the authors read and approved the final manuscript.

Competing interests

The authors declare no competing financial interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture
(2)
College of Life Science and Technology, Huazhong Agricultural University

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