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BMC Plant Biology

Open Access

Whole genome-wide transcript profiling to identify differentially expressed genes associated with seed field emergence in two soybean low phytate mutants

  • Fengjie Yuan1,
  • Xiaomin Yu1,
  • Dekun Dong1,
  • Qinghua Yang1,
  • Xujun Fu1,
  • Shenlong Zhu1 and
  • Danhua Zhu1Email author
BMC Plant BiologyBMC series – open, inclusive and trusted201717:16

https://doi.org/10.1186/s12870-016-0953-7

Received: 16 September 2015

Accepted: 16 December 2016

Published: 18 January 2017

Abstract

Background

Seed germination is important to soybean (Glycine max) growth and development, ultimately affecting soybean yield. A lower seed field emergence has been the main hindrance for breeding soybeans low in phytate. Although this reduction could be overcome by additional breeding and selection, the mechanisms of seed germination in different low phytate mutants remain unknown. In this study, we performed a comparative transcript analysis of two low phytate soybean mutants (TW-1 and TW-1-M), which have the same mutation, a 2 bp deletion in GmMIPS1, but show a significant difference in seed field emergence, TW-1-M was higher than that of TW-1 .

Results

Numerous genes analyzed by RNA-Seq showed markedly different expression levels between TW-1-M and TW-1 mutants. Approximately 30,000–35,000 read-mapped genes and ~21000–25000 expressed genes were identified for each library. There were ~3900–9200 differentially expressed genes (DEGs) in each contrast library, the number of up-regulated genes was similar with down-regulated genes in the mutant TW-1and TW-1-M. Gene ontology functional categories of DEGs indicated that the ethylene-mediated signaling pathway, the abscisic acid-mediated signaling pathway, response to hormone, ethylene biosynthetic process, ethylene metabolic process, regulation of hormone levels, and oxidation-reduction process, regulation of flavonoid biosynthetic process and regulation of abscisic acid-activated signaling pathway had high correlations with seed germination. In total, 2457 DEGs involved in the above functional categories were identified. Twenty-two genes with 20 biological functions were the most highly up/down- regulated (absolute value Log2FC >5) in the high field emergence mutant TW-1-M and were related to metabolic or signaling pathways. Fifty-seven genes with 36 biological functions had the greatest expression abundance (FRPM >100) in germination-related pathways.

Conclusions

Seed germination in the soybean low phytate mutants is a very complex process, which involves a series of physiological, morphological and transcriptional changes. Compared with TW-1, TW-1-M had a very different gene expression profile, which included genes related to plant hormones, antioxidation, anti-stress and energy metabolism processes. Our research provides a molecular basis for understanding germination mechanisms, and is also an important resource for the genetic analysis of germination in low phytate crops. Plant hormone- and antioxidation-related genes might strongly contribute to the high germination rate in the TW-1-M mutant.

Keywords

Low phytate seedSoybeanGerminationField emergenceTranscript

Background

Seeds are important for the survival and evolutionary success of plants and development of human cultures. Their germination traits are traditional agronomic traits and important for crop evolution and development [1, 2]. For soybean breeding and production, low seed germination percentage would decrease the density of soybean seedlings and ultimately affect the yield. Thus, the seed germination percentage and speed should not be negatively affected when developing any ecological, agronomic or nutritional traits.

Lowering the phytate content in crop seeds will be beneficial to improve seed nutritional traits and decrease water phosphorus level [35]. Therefore, there is a considerable interest in generating crops in which phytate synthesis is disrupted during seed development [6]. Seed phytate content can be eliminated by mutation or insertion of transgenes. Many low phytic acid (LPA) mutants have been created in different crops such as rice, maize, soybean and wheat [711]. However, some unwanted traits appeared in these mutants, which hindered the utilization of LPA mutants in crop breeding. Most primary LPA mutants often feature inferior grain yields, reduced seed viability or lower field emergence compared with their respective wild-type parents. Therefore, further improvement is needed before new LPA crops can be put into practical use [1216]. For example, both laboratory and field observations demonstrated that the primary LPA rice mutant lines had lower grain yields and reduced seed viability compared with their respective parental lines [17, 18]. The extensive efforts are still needed in breeding LPA rice cultivars with competitive yields. Some undesirable agronomic and quality traits were also reported in several LPA soybean lines, particularly a lower rate of field emergence [8, 13, 14]. The breeding of high yield and LPA soybean varieties has been hindered by the inherent defects in the LPA mutations. Improving the seed germination trait is an important goal in breeding LPA soybean varieties. However, grain yield and field emergence can be improved through breeding and selection in soybean [15], and indeed, one LPA barley cultivar, CDC Lophy-1 (http://www.inspection.gc.ca/english/plaveg/pbrpov/cropreport/bar/app00006337e.shtml), has already been released for commercial production [16].

We previously developed two LPA mutants in soybean, which involved two non-allelic genes. The LPA traits of the mutant Gm-lpa-TW-1 were due to a 2-bp deletion in the MIPS1 gene; inositol phosphate kinase (GmIPK1) was the mutation’s candidate gene which was related to the low phytate trait in Gm-lpa-ZC-2 mutant. Unlike other LPA mutants, Gm-lpa-ZC-2 appeared to have excellent seed viability (both germination and field emergence) [8]. However, the mutant Gm-lpa-TW-1 revealed a very low field emergence rate, especially in the spring season in Hangzhou, China [8]. Additionally, the seed germination speed decreased quickly during seed storage (unpublished data). Fortunately, an individual plant, which harbored a natural variation and had a significantly higher rate of field emergence, was found among the Gm-lpa-TW-1 lines. According to the results of the MIPS1 gene sequence analysis, the individual has the same mutation site (2 bp deletion in MIPS1) as the Gm-lpa-TW-1 mutant (unpublished data), and it was named Gm-lpa-TW-1-M.

Seed germination is a complex process that includes imbibition, stirring and germination stages, which involve a series of physiological, morphological and transcriptional changes [19]. Several large-scale –omics methods, including transcriptomic, proteomic, and metabolomic methods, have been recently established to investigate the mechanisms of seed germination [20]. The great achievements in soybean genomics have led to application of large-scale gene expression analysis at both mRNA and protein levels to uncover the features of soybean traits. For instance, 69,338 distinct transcripts from 32,885 annotated genes were expressed in soybean seeds which from nine lines varying in oil composition and total oil content [21]. Until now, little is known about the mechanisms responsible for the low seed germination rates in soybean LPA mutants. Although we discovered a soybean LPA mutant with a higher rate of field emergence, seed germination trait is a comprehensive characteristic affected by many factors, including intrinsic and environment cues, during seed developmental and storage stages [22], which makes the genetic analysis of seed germination very difficult. Due to the development of high-throughput deep sequencing approaches, a new method regarding the relationships between gene expression profiles and gene functions has emerged. These technologies are useful for estimating overall gene expression profiles at different developmental stages and/or in different tissues. Although the biochemical pathways that affect seed germination are well characterized, there is still no integrated model describing the differentially expressed genes (DEGs) involved in soybean seed germination, in particular those used in soybean LPA mutant seed germination. The target of this research was to evaluate a large amount of cDNA sequence data, study seed germination trait in detail, and identify candidate genes that could be responsible for LPA soybean germination.

In this study, we used Illumina sequencing to investigate gene expression in soybean LPA mutant seeds at different germination stages and compared transcript reads with the most recent release of the G. max genome sequence (assembly Glyma 1.01).

Methods

Plant material and seed production

Two LPA soybean mutant lines, Gm-lpa-TW-1 (TW-1), Gm-lpa-TW-1-M (TW-1-M) and their wild-type parent Taiwan 75 were used in this experiment to evaluate the seed germination trait. Taiwan 75 is a vegetable soybean variety widely grown in Zhejiang Province. TW-1 was developed using gamma irradiation of wild type Taiwan 75, and TW-1-M was a natural mutant of the TW-1 line. Both TW-1 and TW-1-M had the same phytate content level and mutation site (2-bp deletion in GmMIPS1, unpublished data). Seed samples used for the germination evaluation were harvested from plants grown in neighboring plots in the same field. The seeds were produced in the 2012 spring season in Hangzhou, Zhejiang in the fields of the Experimental Farm of the Zhejiang Academy of Agricultural Sciences.

For differential gene expression detection, we used the two LPA mutants TW-1 and TW-1-M. To better understand and compare the expression differences of mutants TW-1 and TW-1-M during germination, three different germination stages were used in the analysis. These three stages included: the first one is imbibed seeds stage (about 24 h after seeds soaked, named TW-1-1 and TW-1-M-1), second stage is metabolism reactivation phase (about 30 h after seeds soaked), between seed imbibition and radicle emergernce (named TW-1-2 and TW-1-M-2) and the last stage is emergence of primary root which reached 1 mm in length (about 36 h after seeds soaked, named TW-1-3 and TW-1-M-3), three replicates were performed to construct eighteen DGE libraries, they were TW-1-1-1, TW-1-1-2, TW-1-1-3, TW-1-2-1, TW-1-2-2, TW-1-2-3, TW-1-3-1, TW-1-3-2, TW-1-3-3, TW-1-M-1-1, TW-1-M-1-2, TW-1-M-1-3, TW-1-M-2-1, TW-1-M-2-2, TW-1-M-2-3, TW-1-M-3-1, TW-1-M-3-2 and TW-1-M-3-3 . These three stages based on the three phases of germination process (fast water uptake, metabolism reactivation and radicle emergence) were chosen for study according to our germination experiments (as shown below) and some reports [19, 23].

Germination experiments

Two LPA soybean mutant lines and their wild-type parents were used for germination experiments that included two treatments, warm germination and accelerated aging tests. The method was from Meis et al. with a slight modification [13]. For the warm germination, 100 seeds of each line were planted in a Petri dish containing B5 agar gel (50 seeds per 15-mm Petri dish) and were placed in a 25 °C germination chamber in the dark for 4 d. The lines used to evaluate the effectiveness of accelerated aging tests for predicting field emergence after long time storage included the two LPA mutants (TW-1 and TW-1-M) and their wild-type variety Taiwan 75. In total, 200 seeds from each line were placed over 400 ml of distilled water in an acrylic box and covered. The boxes were placed in a chamber at 40 °C for 96 h. The samples were removed from the chamber and planted in the same manner as those from the warm germination. Seed germination in the Petri dishes was defined as the point at which the radical pierced the seed coat. In total, 100 seeds were used per line, per treatment, and three replicates were performed. The experiments were organized in a randomized complete-block design, and the data for each germination test were analyzed by the linear model procedure of SAS statistical software (release 8.02).

Total RNA isolation

Samples from three germination stages were used in this research. Total RNA was isolated using an E.Z.N.A. plant RNA kit (Omega Bio-tek, Inc., USA) according to the manufacturer’s protocol. Genomic DNA contamination was eliminated by RQ1 RNase-Free DNase (Promega, USA).

cDNA library construction and sequencing

The quality of total RNA (OD260/280 = 1.8 ~ 2.2, 28 s/18 s >1.8, and RIN >8) was assessed by using a 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA) and checked using agarose gel electrophoresis. rRNAs were then removed from the total mRNAs in accordance with the instructions included with the Ribo-Zero™ rRNA Removal Kit (Plant Seed/Root) (Epicentre, Madison, WI, USA), final concentration of all RNA samples was adjusted to 500 ng/μl after quantification. cDNA libraries were prepared with the SMART™ cDNA Library Construction Kit, Takara Biomedicak Technology (Beijing) Co.,Ltd and 140–220 bp paired-end reads were generated on the Illumina HiSeq 2000 platform. (Illumina, USA). RNA sequencing was performed by staff at Zhejiang Tiank (Hangzhou, China).

Differential expressed gene detection

Sequencing-received raw image data is transformed by base calling into sequence raw data, and is stored in FASTQ format. All the raw data described in the research from eighteen libraries were published in SRA database. The raw data were filtered by Trimmomatic software to remove adaptor reads, low quality reads (reads containing unknown nucleotides “Ns”), reads of copy number = 1 and reads of lengths less than 20 bp, yielding a dataset consisting of clean reads. For the annotation of reads, clean reads were mapped to the soybean database using software TopHat [24]. Mismatches of no more than two bases were allowed in the alignment. The number of clean reads for each gene was calculated and normalized using a variation of the fragment/Kb/million (FPKM) method. The FPKM method corrects for biases in total gene exon size and normalizes for the total fragment sequences obtained in each tissue library with bioconductor software:cuffquant and cuffnorm. In this experiment, we removed low expression genes which value of FPKM <1 in any library as threshold to count the expressed genes. Principal component analysis between different libraries about their gene expression datasets was carried out by R language software package http://factominer.free.fr/. This method used variance-stabilized data to obtain sample-to-sample distance.

For the tissue-specific analyses, in order to identify differentially expressed genes, cuffdiff software in software packages cufflinks were used to perform pairwise comparisions of stages, and for packages with a corrected P-value of 0.05 and a Log2-fold change [24]. Genes with a P-value < 0.05 and estimated absolute Log2-fold change >1 in sequence counts across libraries were considered to be significantly differentially expressed.

GO enrichment analysis was performed using the SmartGo tool (a software package developed by Tianke company China), we using a hypergeometric (Fisher’s exact test) test to map all DGEs to terms in the Go database (http://www.geneontology.org) to look for significantly enriched Go terms in DGEs comparing to the genome background. The P-value is corrected by Bonferroni, we chose a p value <0.05 as the threshold value. The GO term (P < 0.05) is defined as significantly differentially expressed genes enriched GO term. Pathway enrichment analysis method is the same as that used in the GO analysis. Fisher test was used to check up enrichment gene KEGG pathway with corrected P-value of 0.05.

Quantitative real-time PCR (qRT-PCR)

To validate the data obtained by Illumina RNA-seq, qRT-PCR was performed on 10 genes with log2FC ratios ranging from 2 to 11. At first, we selected ACT11、TUA and CYP2 three housekeeping genes to analyze the stability of their expression using geNorm software (v3.50). The relatively most stable housekeeping gene ACT11 was used to normalize expression levels of selected genes. The RNA samples used for the qRT-PCR assay were the same as those used for the DEG experiments. SYBR Green Real time PCR Master Mix (TOYOBO Biotech Co., Ltd) was used on a Roche (LightCycler®480, USA) instrument according to the manufacturer’s instructions. Each 20-μl reaction comprised a 2-μl template, 10-μl SYBR Green Realtime PCR Master Mix-Plus, 1.2 μl (10 μM) of each primer, 2-μl Plus Solution and 3.6-μl ddH2O. The quantification of gene expression levels was performed in triplicate using the corrected relative –2ΔΔCT method by comparing the data with the internal control gene Act11 [25]. qRT-PCR efficiency was determined by five serial five-fold dilutions of cDNA, and the standard curve confirmed them at high efficiency rates. All primers used for qRT-PCR amplification were designed by Primer Premier 5 and according to the gene mRNA sequence from http://www.ncbi.nlm.nih.gov/genbank/.Primers were synthesized in Shanghai Sangon Biological Engineer Technology and Services Co., Ltd. (Shanghai, China) and are given in Additional file 1.

Results

Seed germination of different soybean mutants

To explore seed germination trait between the mutants and their wild-type parents, we evaluated the germination percentage and speed of soybean lines under both warm germination and accelerated aging test conditions. The goal of the accelerated aging test was to identify the rate at which percent germination declines of soybean lines. The lines that performed well in the accelerated aging test would be expected to maintain viability under prolonged storage.

There were statistically significant differences in the germination speed between TW-1-M and TW-1 in both warm germination and accelerated aging tests (Fig. 1). TW-1-M cost about 72 h to reach the highest germination percentage, whereas TM-1 needed more than 96 h to reach its highest germination percentage point. The germination speeds of TW-1 and Taiwan 75 were the same in both accelerated aging and warm germination tests.
Fig. 1

Changes in germination percentages and germination speed of LPA mutants and their wild-type parents. a germination percentages during warm germination test; b germination percentages during accelerated aging test. In both the warm germination test and accelerating aged test, the mutant TW-1-M performed well, with a high germination percentage (more than 80%) and speed, compared with the TW and wild-type Taiwan 75

In the warm germination test, the germination percentage in TW-1 was very similar to that of its wild-type parent Taiwan 75. The final germination percentage was about 80% according to the germination curve. The germination percentage of TW-1-M was slightly higher than those of the other two lines. The final germination percentage was about 85% (Fig. 1). There was no statistically significant difference between TW-1-M and TW-1. In the accelerated aging test, TW-1-M showed a very good performance in the germination percentage (about 80%). The TW-1 line performed a very low germination percentage (about 45%) and the germination percentage of Taiwan 75 was about 50% (Fig. 1).

The seed germination trait of TW-1-M performed better than those of TW-1 and Taiwan 75. This result indicated that the mutant TW-1-M, with the same mutation site as TW-1, will have significantly better germination trait and storage stability. This result was confirmed by the seed field emergence rate of three lines. The seed field emergence rate of TW-1-M (about 50%) was significant higher than that of TW-1 and Taiwan 75 (less than 10%) after 2-year storage at room temperature.

Digital gene expression (DGE) library sequencing

To characterize gene expression profiles during soybean germination, the high-through put read sequencing analysis of soybean seedling libraries were performed using the Illumina RNA-sequencing analyzer platform. The differences in the gene regulatory pathways between the two LPA mutants were analyzed at the three seed germination stages. The 18 DGE libraries were sequenced and generated approximately 802 million raw reads. All raw data had been published in GEO with accession number GSM2195640 (TW-1-1-1), GSM2195641 (TW-1-1-2), GSM2195642 (TW-1-1-3), GSM2195643 (TW-1-2-1), GSM2195644 (TW-1-2-2), GSM2195645 (TW-1-2-3), GSM2195646 (TW-1-3-1), GSM2195647 (TW-1-3-2), GSM2195648 (TW-1-3-3), GSM2195649 (TW-1-M-1-1), GSM2195650 (TW-1-M-1-2), GSM2195651 (TW-1-M-1-3), GSM2195652 (TW-1-M-2-1), GSM2195653 (TW-1-M-2-2), GSM2195654 (TW-1-M-2-3), GSM2195655 (TW-1-M-3-1), GSM2195656 (TW-1-M-3-2) and GSM2195657 (TW-1-M-3-3). After filtering the low quality reads, the total number of clean reads in each library ranged from 37.7 to 62.1million (Table 1). The percentage of clean reads among the raw reads in most libraries were more than 95%.
Table 1

Library read analyses statistics

 

Total raw reads

Total clean reads

Valid ratio

Gene mapped reads

Gene mapped ratio

Gene unique mapped reads

Gene unique mapped ratio

Non-splice reads ratio

Splice reads ratio

Total reads mapped expressed genes

TW-1-1-1

54846496

53989364

98.44%

47980066

88.87%

40357180

84.11%

55.68%

28.44%

31987

TW-1-1-2

61454226

60684460

98.75%

54536489

89.87%

45869439

84.11%

55.98%

28.13%

32935

TW-1-1-3

46813834

45625642

97.46%

40571957

88.92%

32364788

79.77%

53.88%

25.89%

32613

TW-1-2-1

42866726

41833914

97.59%

36435236

87.09%

29690167

81.48%

54.55%

26.93%

32799

TW-1-2-2

40017620

39514560

98.74%

35947369

90.97%

29879201

83.12%

53.91%

29.21%

33015

TW-1-2-3

44069752

43420982

98.53%

39540531

91.06%

31212328

78.94%

51.35%

27.59%

33479

TW-1-3-1

43704478

42954548

98.28%

38687168

90.07%

31702965

81.95%

53.53%

28.42%

30656

TW-1-3-2

49604414

48907380

98.59%

44561575

91.11%

36044941

80.89%

49.61%

31.28%

33848

TW-1-3-3

44165126

41611906

94.22%

35653176

85.68%

28482253

79.89%

49.44%

30.45%

34820

TW-1-M-1-1

54461020

53698832

98.60%

49119558

91.47%

40110386

81.66%

55.04%

26.61%

32330

TW-1-M-1-2

46244798

45634652

98.68%

41698366

91.37%

35054413

84.07%

55.21%

28.85%

31706

TW-1-M-1-3

53416206

52732078

98.72%

48443543

91.87%

39005105

80.52%

51.91%

28.61%

32999

TW-1-M-2-1

51359166

50426196

98.18%

46437117

92.09%

38158539

82.17%

51.46%

30.71%

33151

TW-1-M-2-2

62294034

61477542

98.69%

56456407

91.83%

46353592

82.11%

50.80%

31.31%

33871

TW-1-M-2-3

43755042

42995004

98.26%

39690338

92.31%

32335903

81.47%

50.00%

31.47%

32406

TW-1-M-3-1

62931516

62062754

98.62%

56549018

91.12%

45018182

79.61%

50.79%

28.82%

34135

TW-1-M-3-2

39450214

37671510

95.49%

32733417

86.89%

25369978

77.50%

49.32%

28.18%

34345

TW-1-M-3-3

46654868

45873652

98.33%

42141833

91.87%

35304742

83.78%

52.61%

31.16%

33738

Mapping reads to the reference transcriptome

Among the clean reads, 88.9–92.3% of transcripts from the TW-1 and TW-1-M mutants were perfectly mapped to the soybean reference genome. The number of unique reads mapped to genes was from 25.4 to 46.4 million. The percentage of these unique reads was about 80%. The number of read-mapped genes ranged from 30,656 to 34,820 (Table 1). The TW-1-M-3 library contained the highest number of read-mapped genes, whereas the TW-1-M-1 library contained the lowest number of read-mapped genes. These data suggested that more genes were expressed in the TW-1-M-3 library compared with the other five libraries.

Variation in gene expression levels quantified by DGE profiles in the LPA mutants

Based on the deep sequencing of the 18 DGE libraries, the number of clean reads in each library was normalized to the FPKM to obtain the normalized gene expression level. Each mapped soybean gene with FPKM value in each of the 18 libraries was listed in the Additional file 2. Twenty thousand eleven (42.7% of reference genes in soybean) and 20,192 (43.1%) expressed genes were detected in the LPA mutants TW-1-1 and TW-1-M-1, respectively. In total, 20,618 (44.0% of reference genes in soybean) and 22,746 (48.5%) expressed genes were detected in the mutants TW-1-2 and TW-1-M-2. Additionally, 19,884 (42.4% of reference genes in soybean) and 23,307 (50.0%) expressed genes were detected in the TW-1-3 and TW-1-M-3. A total of 22,018 genes were expressed in the LPA TW-1 mutants during the whole germination process. Eighteen thousand two hundred four were constitutively expressed, 1727 were stage-specific and 2087 were expressed at the two stages (Fig. 2a). During the seed germination process of TW-1-M, 24,934 genes were expressed. Eighteen thousand six hundred four were constitutively expressed, 2227 were stage-specific and 4103 were expressed at the two stages (Fig. 2b). The number of stage-specific expressed genes in mutant TW-1- 3 was significantly more than in other TW-1 mutants, indicating that TW-1- 3 expressed more specific genes that were related to seed germination. TW-1-M-3 has the most specific and total number of genes expressed compared with the other TW-1-M and TW-1 mutants, and this increased transcript diversity in TW-1-M-3 indicates that it might express a distinctive suite of genes for cellular functions, which may be vital for seed germination.
Fig. 2

Venn diagrams showing the overlapping of expressed genes in LPA mutants. a Venn diagram showing the overlaps in expressed genes among seed germination stages of the TW-1 mutant. b Venn diagram showing the overlaps in expressed genes among seed germination stages of the TW-1-M mutant. c PCA analysis plots for TW-1-M and TW-1 samples. Principal component analysis for the TW-1-M suggests that gene expression in TW-1-3 and TW-1-M-2 is overall similar to each other, TW-1-1 and TW-1-M-1 also have same gene expression pattern. TW-1-2 and TW-1-M-3 perform special gene expression comparing with other four simples, and they also have different gene expression pattern with each other

Furthermore, we analyzed the relationship of different samples with principal component analysis between experiments about their gene expression datasets. Principal component analysis suggested that the gene expression in TW-1-3 and TW-1-M-2 is overall similar to each other. TW-1-1 and TW-1-M-1 also had the same gene expression pattern. TW-1-2 and TW-1-M-3 performed special gene expression compared with other four simples and had different gene expression with each other (Fig. 2c).

Screening of DEGs from massive datasets

To identify and compare the DEGs in LPA mutants during the different germination stages, we used cufflinks software packages to perform pairwise comparisons of DGE libraries between two LPA mutants (TW-1-1 vs.TW-1-M-1, TW-1-2 vs. TW-1-M-2 and TW-1-3 vs.TW-1-M-3). To judge the significance of differences in expressed genes, we used two criteria: P-value < 0.05 and absolute Log2-fold change >1.

In total, there were 3677 DEGs at each germination stage in the LPA mutants (TW-1 and TW-1-M). Among these genes, 3099 (84%) were up-regulated and 548 (16%) were down-regulated in TW-1-M compared with TW-1 (Additional file 3). We found that 1354 genes were down-regulated and 2596 genes were up-regulated in TW-1-M -1compared with TW-1-1. In total, 5060 genes were up-regulated and 4185 genes were down-regulated in TW-1-M-2 compared with TW-1-2. Additionally, 3395 genes were down-regulated and 1983 genes were up-regulated in TW-1-M-3 compared with TW-1-3 (Additional file 3, Fig. 3).
Fig. 3

Cufflinks volcano plots of differentially express gene. a-c Cufflinks valcano plots for each contrast library showing variances in gene expression with respect to fold-change and significance. Each dot represents an individual gene. Black dots represent genes with no significantly differentially expressed, green dots represent significant down-regulated DEGs and red dots represents significant up-regulated DEGs

To illustrate differences between different libraries, heat maps were constructed using heatmap.2 software showing log10 (FPKM) expression values for top 500 of the most differentially expressed genes in six contrast libraries. The results showed that expression level of the top DEGs in TW-1-M-1 was different from TW-1-1 (Fig. 4a). Furthermore, expression level of 500 DEGs in TW-1-M-2 was different from TW-1-2. Most of genes showed completely contrary expression level in these two contrast libraries (Fig. 4b). We concluded that TW-1-M-2 was different from TW-1-2 during seed germination. On the contrary, the expression levels of many top DEGs were similar with each other between TW-1-M-3 and TW-1-3 libraries (Fig. 4c). It should be noted that the expression level of DEGs which related with germination in these two mutants becomes similar in this germination stage.
Fig. 4

Heat map generated from the top 500 DEGs as reported by heatmap.2 in R language. The red color indicated higher levels of gene expression while green indicated lower expression by log10 (FPKM). The results showed that expression level of 500 DEGs inTW-1-M-2 were very different from TW-1-2, on the contrary, many top DEGs’ expression level were similar with each other between TW-1-M-3 and TW-1-3 libraries

Further analysis of DEGs between the two genotypes

Based on the results of the two mutants, DEGs were further compared between TW-1 and TW-1-M. In total, 190 genes were expressed only in the TW-1-1 vs TW-1-M-1 library and most of them were up-regulated in TW-1-1; 616 genes were found in the TW-1-2 vs TW-1-M -2 library and 65.6% of them were up-regulated in TW-1-M-2; 169 genes were specifically expressed in the TW-1-3 vs TW-1-M-3 library and 67.5% of them were up-regulated in TW-1-M-3 (Additional file 4). These genes might have special functions leading to different seed germination traits.

GO functional enrichment analysis of DEGs in the different libraries from LPA mutant genotype

GO encompasses three domains: cellular component, biological process and molecular function. The basic GO unit is the GO term. Every GO term belongs to a particular category. GO terms with Bonferroni-corrected P-values < 0.05 were defined as being significantly enriched in DEGs.

In our study, most DEGs regardless of regulation direction from different libraries were involved in the categories of nucleus (GO:0005634), cell part (GO: 0044464), plastid (GO:0009536), membrane (GO:0016020) and intergral component of membrane (GO:0016021) with respect to cellular components. Under the biological process, most of the DEGs could be divided into five categories, metabolic process (GO:0044260, GO:0044710 and GO:0019538), oxidation-reduction process (GO:0055114), response to environmental stimulus, plant hormone signaling pathway. With regard to molecular function, DNA, RNA and protein binding are the largest DEG categories, and oxidoreductase activity is also a very important functional group (Fig. 5, Additional file 5).
Fig. 5

Functional categorization of significantly DEGs during the seed germination stage. a Functional categorization of significantly DEGs between TW-1-M-1 and TW-1-1. b Functional categorization of significantly DEGs between TW-1-M-2 and TW-1-2. c Functional categorization of significantly DEGs between TW-1-M-3 and TW-1-3

Seed germination is a complex process which involved in many activities of some key enzymes in glycolysis, pentose phosphate pathway, the tricarboxylicacid cycle, protein and lipid metabolism [19]. Furthermore, reactive oxygen species production including the superoxide anion radical, hydrogen peroxide and the hydroxyl radical can cause oxidative damage to cellular components and reduce seeds ability to germination [26, 27]. Finally, plant hormones such as abscisic acid, gibberellins and ethylene play a very important role in seeds germination [28]. According to above researches and the DGEs numbers in functional categories, in this research, we were concerned with the functional categories of DEGs (regardless of regulation direction) related to seed germination process. In the contrasting groups TW-1-1 and TW-1-M-1, the DEGs most possibly related to seed germination were from the biological process category: oxidation-reduction process (GO:0055114), protein metabolic process (GO:0019538), carbohydrate metabolic process (GO:0005975), lipid metabolic process (GO:0006629) and hormone transport (GO:0009914). Biological processes, which would be responsible for seed germination in contrasting groups TW-1-2 and TW-1-M-2 were classified in oxidation-reduction (GO:0055114), protein metabolic process (GO:0019538), lipid metabolic process (GO:0006629), response to hormone (GO:0009725), regulation of hormone levels (GO:0010817) and carbohydrate metabolic process (GO:0005975). We also found some biological processes involved with seed germination in the TW-1-3 and TW-1-M-3 groups, including the oxidation-reduction process (GO:0055114) and seed germination (GO:0009845). These biological processes might be highly related to seed germination traits (Fig. 5, Additional file 5).

Pathway enrichment analysis of DEGS

A pathway enrichment analysis is an effective method to elucidate DGE biological functions. A pathway-based analysis can identify significantly enriched metabolic and signal transduction pathways in DEGs by comparing their whole-genome backgrounds [29]. The formula used for this calculation was essentially identical to that used in the GO analysis, with pathways having P-values < 0.05 being defined as significant DEGs.

DEGs in 80 metabolic and signal transduction pathways were found between TW-1-1 and TW-1-M-1 contrast libraries. The mainly regulated pathways with the most up-regulated gene numbers in TW-1-M-1 were ‘biosynthesis of secondary metabolites’, ‘plant hormone signal transduction’, ‘Ascorbate and aldarate metabolism’ and ‘starch and sucrose metabolism’. There were 113 enrichment pathways involved in the TW-1-2 and TW-1-M-2 contrast libraries. Among these pathways, six pathways might be related to seed germination, ‘biosynthesis of secondary metabolites’, ‘starch and sucrose metabolism’, ‘flavone and flavonol biosynthesis’, ‘isoflavonoid biosynthesis’ and ‘plant hormone signal transduction’ and ‘gulutathione metabolism’. These pathways were up-regulated in TW-1-M-2. The most enriched pathways responsible for seed germination in the TW-1-M-3 vs TW-1-3 contrast libraries were ‘plant hormone signal transduction’ and ‘starch and sucrose metabolism’. These two pathways performed two contrast regulation directions, some genes were up-regulated and the others were down-regulated (Fig. 6, Additional file 6).
Fig. 6

Scatter diograms illustrating the pathway enrichment analysis. a down-regulated enrichment pathway items in TW-1-M-1 relative to TW-1-1. b up-regulated enrichment pathway items in TW-1-M-1 relative to TW-1-1. c down-regulated enrichment pathway items in TW-1-M-2 relative to TW-1-2. d up-regulated enrichment pathway items in TW-1-M-2 relative to TW-1-2. e down-regulated enrichment pathway items in TW-1-M-3 relative to TW-1-3. f up-regulated enrichment pathway items in TW-1-M-3 relative to TW-1-3

DEGs analysis in each category regarding seed germination-related biological processes in the LPA mutant TW-M

GO functional annotations and a pathway enrichment analysis of DEGs (regardless of directions) in the high germination mutant implied that the DEGs in the most highly enriched biological processes and pathways were most likely contributing to the good seed germination trait. Because we are interested in seed germination-related biological processes, we focused on the DEGs involved in pathways and functional categories related to seed germination biological processes. All of these DEGs are listed in Additional file 7.

In total, 527 DEGs in the TW-1-1 and TW-1-M-1 contrast libraries were related to seven different biological processes. Among these genes, 97 DEGs were down-regulated and 213 DEGs were up-regulated in oxidation-reduction process in mutant TW-1-M-1, other 384 DGEs were all up-regulated in TW-1-M-1 in hormone-mediated signaling pathway, auxin-activated signaling pathway, response to auxin, auxin transport, hormone transport, gibberellic acid mediated signaling pathway, gibberellin mediated signaling pathway and gibberellin biosynthetic process.

In total, 1240 DEGs between the TW-1-2 and TW-1-M-2 contrast libraries could be separated into five functional categories, including the response to hormone, ethylene biosynthetic process, ethylene metabolic process, regulation of hormone levels, and oxidation-reduction process. Of these, 54 genes were up-regulated in the hormone biosynthetic process of TW-1-M-2 and 69 genes related to hormone metabolic process were also up-regulated in TW-1-M-2. The most DEGs were found in the oxidation-reduction process, with 408 up-regulated genes in TW-1-M-2. In total, 880 down-regulated genes were found in TW-1-M-2 mutant in ten different functional categories, including hormone-mediated signaling pathway (225 DEGs), response to hormone (202 DEGs), response to abscisic acid (111 DEGs), ethylene-activated signaling pathway (83 DEGs), abscisic acid-activated signaling pathway (81 DEGs), response to ethylene (60 DEGs), ethylene biosynthetic process (36 DEGs), ethylene metabolic process (36DEGs), regulation of flavonoid biosynthetic process (27 DEGs) and regulation of abscisic acid-activated signaling pathway (19 DEGs).

The 690 DEGs in the TW-1-3 and TW-1-M-3 contrast libraries were divided into seven functional categories, including the hormone-mediated signaling pathway (178 DEGs), response to abscisic acid (78 DEGs), ethylene-mediated signaling pathway (65 DEGs), oxidation-reduction process (232 DEGs), abscisic acid-activated signaling pathway (59 DEGs), response to ethylene (47 DEGs) and seed germination (31 DEGs). Of these, all genes were down-regulated in the TW-1-M-3 mutant (Additional file 7).

Possible DEGs for major roles in response to better seed germination trait

The 22 most DEGs (absolute value Log2FC >5) were identified by a DGE analysis of the TW-1-M (Additional file 8). Among them, 13 genes were up-regulated and nine genes were down-regulated. These included three transcription factors, one cytochrome gene, two auxin-induced protein genes, four oxidase genes, one isoflavone 7-0-methyltransferase gene, six genes related with carbohydrate metabolism, two genes which catalyzed the glutathione metabolism, one expansin gene and two other genes. The functional annotations of these genes are shown in Table 2. These genes might be the most important genes contributing to the high seed germination percentage and speed in TW-1-M.
Table 2

Most differentially expressed genes identified by DGEs analysis in TW-1-M relative to TW-1

Gene

Gene annotation

Regulation direction

gene21202

1-Cys peroxiredoxin

down

gene26324

auxin-induced protein 15A

up

gene30929

auxin-induced protein 15A-like

up

gene30772

cytochrome P450 CYP82D47-like

down

gene47663

expansin-A1-like

down

gene22557

F-box/kelch-repeat protein At3g23880-like

up

gene22518

GATA transcription factor 7-like

down

gene8527

glutathione synthetase, chloroplastic-like

down

gene23718

hydroquinone glucosyltransferase-like

up

gene52332

isoflavone 7-O-methyltransferase-like

up

gene35300

late embryogenesis abundant protein-like

down

gene30019

oxidoreductase

down

gene43660

polygalacturonase inhibitor-like

up

gene37153

polyphenol oxidase A1, chloroplastic-like

up

gene24050

probable F-box protein At4g22030

down

gene18907

probable glutathione S-transferase

up

gene36822

probable glycosyltransferase At5g03795

down

gene42818

reticuline oxidase-like protein

up

gene51756

soyasapogenol B glucuronide galactosyltransferase-like

up

gene53209

two-component response regulator ARR14-like

up

gene6319

UDP-glycosyltransferase 72D1-like

up

gene51015

UDP-glycosyltransferase 91A1-like

up

In this study, we also analyzed some DEGs with high FPKM values (FPKM value in library TW-1 or TW-1-M more than 100) (Additional file 9), these genes could be divided into eight groups. The first group contained 13 genes which mainly participated in carbohydrate metabolism (glycosyltransferase, glucanase, galactinol synthase). The second group was composed of 11 genes, they were one abscisic-acid-receptor, two auxin –regulated protein, six ethylene-responsive transcription factors, one gibberellin 2-beta-dioxygenase and one gibberellin-regulated protein. The third group contained 10 transcript factors. The fourth group were made up of nine oxidoreductases (three carboxylateoxidase, one acyl-CoA oxidase, one ascorbate peroxidase, one L—ascorbate oxidase, two peroxidase and one aldo-keto reductase). The fifth group constituted five glutathione S-transferases. The sixth group inculded four embryogenesis protein genes. The seventh group was made up three flavonol genes. The last group comprised two catalase genes (Table 3).
Table 3

DEGs with high FRPM values in TW-1 and TW-1-M

Gene

Gene_annotation

TW-1-M

TW-1

Regulation

gene13922

1-aminocyclopropane-1-carboxylate oxidase 1

1160.28

240.427

up

gene20644

1-aminocyclopropane-1-carboxylate oxidase 1-like

326.818

56.2496

up

gene20259

1-aminocyclopropane-1-carboxylate oxidase-like

446.817

204.574

up

gene2676

2-hydroxyisoflavanone dehydratase

148.017

308.306

down

gene34732

ABC transporter F family member 1, transcript variant X1

57.2258

240.966

down

gene37621

abscisic acid receptor PYL12-like

656.208

1350.98

down

gene12186

acyl-CoA oxidase

55.391

117.437

down

gene58187

ascorbate peroxidase 1, cytosolic

270.578

105.846

up

gene29377

auxin down-regulated protein

2132.82

759.573

up

gene37700

auxin-repressed 12.5 kDa protein-like, transcript variant X1

116.909

278.5

down

gene8950

catalase

62

192.975

down

gene14347

catalase

90.0193

255.074

down

gene7840

dolichyl-diphosphooligosaccharide--protein glycosyltransferase subunit 4A

236.779

110.339

up

gene54561

dolichyl-diphosphooligosaccharide--protein glycosyltransferase subunit 4A

223.879

104.715

up

gene27626

embryonic protein DC-8-like

44.7358

269.219

down

gene31117

endo-1,3-beta-glucanase

59.2309

540.571

down

gene37696

ethylene-responsive transcription factor 12

212.678

879.038

down

gene42281

ethylene-responsive transcription factor 12-like

374.843

794.408

down

gene15603

ethylene-responsive transcription factor ERF110-like

155.112

347.213

down

gene49187

ethylene-responsive transcription factor RAP2-1-like

56.2688

135.164

down

gene24409

ethylene-responsive transcription factor RAP2-3-like

668.14

1576.5

down

gene42969

ethylene-responsive transcription factor RAP2-3-like

1167.92

3510.15

down

gene35673

F-box protein SKP2B, transcript variant X1

77.6451

172.134

down

gene55554

flavonol synthase/flavanone 3-hydroxylase-like

84.0581

183.276

down

gene8335

galactinol synthase 1

500.381

1388.7

down

gene55115

galactinol synthase 2

23.0731

379.858

down

gene15912

galactinol--sucrose galactosyltransferase, transcript variant X2

142.3

396.706

down

gene11959

galactinol--sucrose galactosyltransferase-like

90.2918

344.135

down

gene42444

gibberellin 2-beta-dioxygenase-like

240.649

96.8673

up

gene52252

gibberellin-regulated protein 4-like

6582.18

2751.57

up

gene56620

glutathione S-transferase GST 18

74.9299

251.187

down

gene18908

glutathione S-transferase GST 7

223.983

96.0631

up

gene27455

glutathione S-transferase L3, transcript variant X1

158.692

512.444

down

gene44166

glutathione transferase

132.399

308.722

down

gene21642

homeodomain-leucine zipper protein 56, transcript variant X1

189.04

91.3283

up

gene46225

isoflavone 7-O-glucosyltransferase 1-like

30.5776

116.967

down

gene8972

L-ascorbate oxidase homolog

139.264

63.2909

up

gene27264

late embryogenesis abundant protein D-34-like

300.459

1242.39

down

gene35300

late embryogenesis abundant protein-like

328.87

2372.13

down

gene37327

late embryogenesis abundant protein-like

52.5198

587.359

down

gene30694

MYB transcription factor MYB50

142.722

41.363

up

gene9199

MYB transcription factor MYB68

52.8014

152.65

down

gene46117

peroxidase 12-like

192.985

80.4326

up

gene24224

peroxidase 15-like, transcript variant X1

179.556

36.2356

up

gene16824

probable aldo-keto reductase 2

42.1291

513.099

down

gene47762

probable galactinol--sucrose galactosyltransferase 6, transcript variant X1

161.157

60.6944

up

gene44169

probable glutathione S-transferase parC

86.7473

287.001

down

gene8658

putative F-box protein PP2-B12

77.8839

458.651

down

gene11905

RING-H2 finger protein ATL48

269.579

746.254

down

gene55008

stachyose synthase

57.1634

224.996

down

gene27675

transcription elongation factor 1 homolog

129.432

322.136

down

gene12634

transcription factor HEC1-like

80.3

168.632

down

gene30355

UDP-glucose 4-epimerase GEPI48-like, transcript variant X1

48.3533

148.505

down

gene52306

UDP-glucose 6-dehydrogenase 1

172.346

65.6367

up

gene22682

UDP-glucose dehydrogenase, transcript variant X1

181.645

84.1597

up

gene54704

UDP-glycosyltransferase 73C2-like

392.204

114.358

up

gene45

zinc finger CCCH domain-containing protein 20

97.306

265.596

down

Confirmation of read-mapped genes by qRT-PCR

To certify the reliability of the Solexa/Illumina sequencing technology, 10 genes were selected for qRT-PCR assays. The soybean ACT11 gene was used as an internal control. Although the qRT-PCR expression data was not very consistent with the data from the Solexa RNA-seq analysis, both methods yielded the same expression trends (Fig. 7).
Fig. 7

Results of qRT-PCR on 10 genes. a Glyma05g31370; b Glyma01g12970; c Glyma06g02040; d Glyma15g03650; e Glyma03g41920; f Glyma08g08620; g Glyma08g14630; h Glyma13g22060; i Glyma13g30210; j Glyma17g34800. Expression data from qRT-PCR basically corroborated the data from Solexa RNA-seq analysis, with both methods yielding the same expression trends

Discussion

In this study, numerous genes showed different expression levels between TW-1-M and TW-1 mutants. These expression differences were analyzed by RNA-Seq, a fully quantitative method for gene expression evolution [30], providing a new platform to understand the relationships between germination processes and regulatory mechanisms. In our experiments, the number of up-regulated genes was significantly higher than the number of down-regulated genes in TW-1-M, which indicated that most of the genes related to the better germination process and regulatory mechanism were up-regulated. Based on the detailed analysis of high germination-related functionally annotated genes and pathways, genes with the greatest significant differences in expression or abundance were found. These genes were mainly involved in anti-stress, plant hormones, reactive oxygen species and energy metabolism processes. These results were partly consistent with the results from other plants, such as wheat [19], garden pea [31], Arabidopsis [32] and rice [33]. According to these reports [19, 3133], the genes related to plant hormones and reactive oxygen species might play key roles in seed germination. No report was found on the transcriptomes of LPA crops, especially during the germination process. However, in LPA maize, the free radicals content increased and the seed antioxidation ability decreased because of the reduction in phytate [34], indicating that the genes with antioxidation abilities might be responsible for seed germination rates in LPA crops.

DGE responses to stress

Based on the changes in the external environment, soybean seeds could use different mechanisms to cope with many biotic and abiotic stresses during germination [35]. In this research, we found many GO functional categories related to abiotic stresses, such as response to salt stress, response to stress and response to heat, even though we performed all of the experiments under the same environmental conditions. These two LPA mutants had a different regulatory response mechanism to the germination environment although their seeds having the same phytate levels and mutated gene. Ten other transcription factors with high levels of differential expression were identified by the DGE analysis in TW-1. These could also participate in the regulation of genes involved in responding to stress and the seed germination process. The high expression level of these genes in the mutant TW-1 might suggest that TW-1 seeds have a different ability to overcome stresses comparing with mutant TW-1-M during the germination stage.

DGE responses to plant hormones

Seed germination is controlled by both intrinsic and environmental cues, which are mainly regulated by two antagonistic phytohormones, abscisic acid (ABA) and gibberellin (GA) [20]. GA promotes seed germination, whereas ABA has a contrary effect [36]. In our research, we also found three candidate genes, one gibberellin 2-beta-dioxygenase, one gibberellin-regulated protein and ABA receptor PYL12, involved in GA and ABA metabolism and signal transduction (Table 3). Gibberellin-regulated protein may function at hormonal-controlled steps of development such as seed germination, flowering and seed maturation [37]. GA2ox is responsible for seed dormancy and germination during dark imbibition [38]. These two genes were up-regulated in TW-1-M. PYLs function as ABA receptors in the ABA signaling pathway [39], and PYLs-mediated ABA signaling could play a crucial role in favoring stress adaptation and growth development for plants [40]. In our results, Gibberellin-regulated protein had the greatest expression abundance, especially in TW-1-M, which could be responsible for TW-1-M’s high germination.

Another plant hormone, ethylene, which participates in the regulation of GA and ABA, could also be responsible for seed germination [41]. In our study, we also found some ethylene regulatory and biosynthesis genes (six ethylene-responsive transcription factors and three 1-aminocyclopropane-1-carboxylate oxidases), which had high expression abundances in the six constructed libraries. All ethylene-responsive transcription factors were down-regulated in TW-1-M, three 1-aminocyclopropane-1-carboxylate oxidases genes were up-regulated in TW-1-M.

DGE responses to reactive oxygen species

The successful execution of a germination program depends greatly on the seed oxidative homeostasis [26]. Many functional genes and pathways involved in the soybean oxidative process were identified. These genes maintain oxidative balances and reduce oxidative damage to a wide range of cellular components, including DNA, proteins and lipids, and maintain the seeds’ ability to germinate [4244]. The ascorbate peroxidase gene and L-ascorbate oxidase gene, with their high expression abundances in TW-1-M, play an important role in the regulation of the oxidative state, protecting seeds and maintaining their vigor in mature drying seeds as well as during the early stages of germination [25, 45]. The most differentially expressed gene, Cytochrome P450, were found in both TW-1 and TW-1-M. The P450 family is a large and diverse group of isozymes that mediate a diverse array of oxidative reactions [46]. Polyphenol oxidase A1 was highly expressed in TW-1-M, revealing a vital defense function and protective role in the sensitive early phase of germination and seeding development [47]. The enzyme catalase, which has been employed to determine seed viability [48], was also identified as highly expressed in mutant TW-1. Some DEGs related to flavone metabolism were identified, such as 2-hydroxyisoflavanone synthase, an isoflavone reductase homolog, isoflavone 2’-hydroxylase and isoflavone reductase. These genes are involved in isoflavone biosynthesis, and isoflavone is regarded as an anti-oxidative compound in soybean seeds.

DGE responses to energy metabolism

Respiration and energy production play key roles in whole seed germination [19]. In this research, some highly expressed and enriched genes related to carbohydrate biosynthesis and metabolism pathways were found, such as glycosyltransferase, glucanase, galactinol synthase, and glucose. These genes might provide energy, translate signaling and be involved in the anti-oxidative process during seed germination.

We also found some embryogenesis abundant protein genes were high expressed in mutant TW-1. Although we compared the transcripts of soybean LPA mutants, we did not find any DEGs related to the phytate metabolic process. This result indicated that these two mutants used the same phytate metabolic pathway in seeds during the germination stage. We identified some candidate genes that might strongly influence seed germination in TW-1-M. However, we still need to perform a genetic analysis and gene mapping to clone these new genes. Further research will help understand the differences in seed germination between the two LPA mutants.

Conclusions

Improving the seed germination trait of LPA crops is an important goal in crop breeding programs. The gene expression profiling of LPA soybean mutants should provide a substantial contribution to understand the germination mechanism in LPA crops.

In this study, 3,950-9,245 DEGs were identified in each contrast libraries, with TW-1-M having the similar up- and down- regulated DEGs with TW-1. TW-1-M and TW-1 displayed many differentially expressed transcripts involved in seed germination, and DEGs from the seed germination process were mainly related to the ethylene-mediated signaling pathway, oxidation-reduction, the abscisic acid-mediated signaling pathway, response to hormone, ethylene biosynthetic process, ethylene metabolic process, regulation of hormone levels, and oxidation-reduction process, regulation of flavonoid biosynthetic process and regulation of abscisic acid-activated signaling pathway. In total, 2457 DEGs involved in the functional categories above were identified. Twenty-two genes with 20 biological functions were most differentially expressed in high germination-related metabolic or signaling pathways. Fifty-seven genes with 36 biological functions had the greatest expression abundance in germination-related pathways. TW-1-M showed high gene expression in anti-oxidation, GA biosynthesis, stress response and energy metabolism processes, but low gene expression levels in ethylene synthesis during seed germination. The differences in these biological processes between the two LPA mutants could provide a molecular basis for the difference in the seed germination rate.

The findings of this research will allow us to further understand the molecular mechanisms of seed germination in LPA crops. These can also be used as an important resource for the genetic analyses of LPA crop germination traits. Our work suggested that expression diversification of plant hormone- and reactive oxygen species-related genes might strongly contribute to the successful germination in TW-1-M.

Abbreviations

ABA: 

Abscisic acid

DEGs: 

Differentially expressed genes

DGE: 

Digital gene expression

FPKM: 

Fragment/Kb/million

GA: 

Gibberellin

LPA: 

Low phytic acid

Declarations

Acknowledgements

This research was supported by the Program form the National Natural Science Foundation of China (No. 31271754) to FJY. Our heartfelt thanks go to the anonymous reviewers who offered their cirtical connments for the improvement of this paper.

Funding

This research was supported by the Program form the National Natural Science Foundation of China (No. 31271754). The funding body supported the study, analysis of data and writing the manuscript.

Availability of data and materials

The data supporting the findings can be found in the manuscript, supplementary files. The datasets generated during the current study are available in the SRA repository and accession number were GSM2195640 (TW-1-1-1), GSM2195641 (TW-1-1-2), GSM2195642 (TW-1-1-3), GSM2195643 (TW-1-2-1), GSM2195644 (TW-1-2-2), GSM2195645 (TW-1-2-3), GSM2195646 (TW-1-3-1), GSM2195647 (TW-1-3-2), GSM2195648 (TW-1-3-3), GSM2195649 (TW-1-M-1-1), GSM2195650 (TW-1-M-1-2), GSM2195651 (TW-1-M-1-3), GSM2195652 (TW-1-M-2-1), GSM2195653 (TW-1-M-2-2), GSM2195654 (TW-1-M-2-3), GSM2195655 (TW-1-M-3-1), GSM2195656 (TW-1-M-3-2) and GSM2195657 (TW-1-M-3-3).

Authors’ contributions

FY designed the study and drafted the manuscript. XY participated in the data analysis and helped draft the manuscript. DD performed the statistical analysis and helped draft the manuscript. QY carried out the qRT-PCR work. XF participated in planting mutants and collecting the materials. SZ carried out the seed germination experiment. DZ participated in designing the study and drafting the manuscript. All authors have read and approved this manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

We confirmed that this article does not contain any studies with human participants or animals performed by any of the authors and is in compliance with ethical standards for research. This statement was made by ethics committee of institute of crop science and nuclear technology utilization, Zhejiang Academy of Agricultural Sciences.

Research involving plants

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)
Institute of Crop Science and Nuclear Technology Utilization, Zhejiang Academy of Agricultural Sciences

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