Carbohydrate is one of the most important products of plant photosynthesis, which not
only participates in plant morphogenesis, but also serves as an important
energy supply during plant growth and development (Raessler et al. 2010). Especially, as an
important indicator of plant carbon absorption and consumption, sugar is both
photosynthetic product and respiratory substrates, which provide carbon
skeletons and energy for plants. Sugar is also a precursor for the synthesis of
many plant secondary metabolites, such as phenols, defense and aromatic
compounds (Li et al. 2020).
Furthermore, sugar is also believed to be a signaling molecule that regulates
plant development and defense responses (Ruan et al. 2010; Cho and
Yoo 2011). Therefore, the
synthesis and metabolism of carbohydrates, especially sugars, affect the whole
process of plant growth and development. Sugar accumulation is critical to crop
yield and quality formation, and appropriate sugar-acid ratio is also the core of fruit commercial quality in fruit trees.
Highbush blueberry, the shrub of
Ericaceae family, is a kind of the most famous commercial berry in the world (Kim et al. 2013). For the past few years, blueberries have received a lot of
attention for their powerful antioxidant properties and abundant active
ingredient, such as high levels of anthocyanins, flavonols and
proanthocyanidins (Prior et al. 2001; Castrejó et al. 2008; Vrhovsek et al. 2012; Gibson et al. 2013). In
blueberry, the coordinated changes in carbohydrates and anthocyanins are the most obvious
aspects of the complex process in fruit maturation, containing flavor and
nutritional transformation, such as sweetness and color changes (Zifkin et al. 2012). The fruit ripening is accompanied by rapid changes in color,
taste, flavor, and nutrients in blueberry, which caused by the accumulation of
large amounts of certain metabolites such as sugar, organic acids, phenolic
compound, and especially anthocyanins (Rasmussen
et al. 2005). The contents of these
metabolites vary over time, which is attributed to the different rates of
synthesis, degradation and transportation during fruit ripening. The degree of
sugar accumulation largely determines the sweetness of the fruit at harvest (Kader
2008). The TCA cycle and GABA shut also
determines the fruit acidity (Liu et al. 2007; Zhang et al. 2014), and flavonoid accumulation not only makes the fruit appear blue,
but also is the main reason for the formation of antioxidant activity in
blueberry (Zifkin et al. 2012; Harb et al. 2010). Carbohydrate metabolism and flavonoids synthesis have been the
focus of fruit quality research. However, the metabolism and accumulation of
such substances is a complex physiological process, the mechanism of
carbohydrate metabolism and flavonoid synthesis and key enzyme genes have not
yet been identified in blueberry fruit maturation.
Blueberry ploidy is highly
heterozygous and has a large genome (1216 Mbp) size (Costich et al. 1993; Bian et al. 2014; Colle et al. 2019). The short of information about blueberry genome sequences affects our
understanding of molecular information about key quality changes during
ripening. High throughput omics provides reliable way for studying
the complex regulatory processes about metabolic pathways, gene expression or
gene regulatory networks (Wang et al. 2017). Currently, transcriptome and multiple omics studies based on high
throughput sequencing have been applied to research growth and development and
the physiological synthesis of active substances in blueberry, but mainly
focused on the study of plant cooling capacity (Naik et al. 2007; Dhanaraj et al. 2007), flower bud differentiation (Albert et al. 2005), and antioxidant metabolism of fruit polyphenols (Li et al. 2012; Lin et al. 2018; Li et al. 2019). There are no reports on the mechanism of fruit quality formation,
most of related enzyme genes have not been identified. The gene expression
pattern at different developmental stages and genotypes need to be further
verified.
In order to confirm the optimal
harvest time, obtain better taste and more nutritious varieties, we identified
key enzyme genes encoding carbohydrate metabolism and accumulation of
flavonoids biosynthesis based on transcriptome sequencing and gene expression
analysis of different two varieties at three development period. We tentatively
speculated the role of these genes in blueberry fruit maturation. The purpose of this research was to explore the main mechanism of fruit
quality formation in blueberry.
Materials and Methods
Plant material
The
blueberry fruits (Vaccinium corymbosum
L. cvs. ‘Spartan’ and ‘Raka’) used in this study were
collected from an greenhouse in Liaoning Institute of Pomology, XiongYue, Liaoning Province, China. Five
trees of 5-year-old from each cultivar were chosen to exclude stochastic error
caused by different genetic and environment backgrounds. Fruit of three
different stages, including green, pink and blue fruit stage (42, 49 and 56
days after full bloom), were gathered from ten different trees, denoted as S1, S2, S3, R1, R2, R3
(Fig. 1). All fruits were flash-frozen in liquid nitrogen and stored in
ultra-low temperature freezer at -80ºC after were transported to the
laboratory.
Soluble sugar content (SSC) and titratable acid (TA) measurement
The
mixed juice extracted from ten fruit in same developmental period to measure
SSC using PAL-LOOP (Atago Japan). The SSC of the fruit was represented by the
mean of three replications. TA was measured using NaOH titration according to
method of Shiraishi (1995).
Total anthocyanin content (TAC) and total phenols content (TPC)
measurement
The
extraction of TAC and TPC followed the modified methods of Kim et al.
(2013). Ten frozen fruits of each sample were mixed and ground into powder in
liquid nitrogen. Then 5 g powder was added to ethanol, distilled water and
hydrochloric acid solution (70:30:1, v/v/v), and centrifuged for 20 min. After
repeat centrifugation for twice, combined solution, and set the total volume to
20 mL using for testing.
The TAC was measured using pH differentia
method (Connor et al. 2002).
The extracting solution was diluted by acidic methanol (1:99, v/v), the
absorbance value was determined at 530 nm. The standard curve was established
with cyanidin-3-glucoside as the standard substance, and the total anthocyanin
content was expressed as mg/100g standard substance equivalents.
The TPC was measured using
Folin-Ciocalteu method (Pastrana-Bonilla
et al. 2003).
The extracting solution (0.04 mL) was added Folin-Ciocalteu (1 mL), 75% sodium
carbonate (0.8 mL) and distilled water (0.16 mL). After mixed and incubated for
30 min, the absorbance value was determined at 765 nm. The standard curve was
established with gallic acid as the standard substance, and the total phenols
content was expressed as mg/100g standard substance equivalents.
RNA extraction, cDNA library construction and RNA-seq
Total
RNA was extracted using the modified CTAB method (Jaakola et al. 2001).
After treated using DNase I, the samples were enriched with magnetic beads and
broken into mRNA short fragments. These short fragments were reverse
transcribed into cDNA, and proceeded with the purification of double-stranded,
terminal repair, addition of A tail and connection. About 200–700 bp fragments
were separated and used as templates for PCR amplification to obtain the cDNA
library. Six libraries were tested the sequence from double-ended on the
Illumina HiSeq™2000 platform.
Sequence assembly and annotation
The
original image data file from RNA-Seq via CASAVA bases to identify analysis
into the original data (raw data). After detecting the error rate, ATGC
distribution, and filtering the reads containing adapter, ploy-N and low
quality from raw data, we finally obtained the clean reads. The subsequent
analysis was based on clean reads. Trinity was used to transcriptome assembly
with min_kmer_cov set as two and all of other arguments set as default (Grabherr et al. 2011). The sequences of six samples
were mixed and spliced to serve as genome reference sequences.
Based on the NR, NT, Swiss-Prot,
Pfam, gene ontology (GO), KOG, and KEGG Ortholog databases
(KO), function annotation information of genes were obtained. The amino acid
sequences and the coding regions information of genes was predicted using
BLAST. ESTScan software was used to predicted others sequence information, when
its none annotated above databases (Iseli et al. 1999).
Differential expression analysis
Clear
reads of each sample were mapped back onto the reference sequence to calculate
the read-count (Li and Dewey 2011). Considering the impact of sequencing depth
and gene length, we transformed readcount into RPKM (Mortazavi
et al. 2008)
to expression the relative expression levels.
DEGs of two samples were identified using the DEGseq
software. To control false positives, p-value
and log2 (fold change) were used to screen the DEGs. In this study,
DEGs need to be matched q-value
≤0.005 and |log2 (fold change)| ≥1, q-value was adjusted using q value (Storey 2003).
Functional enrichment analysis
To comprehend the profile and function of DEGs that were
obtained on different samples, we performed gene ontology analysis and
conducted the functional enrichment using GOseq software (Young et al. 2010). In pathway analysis, all DEGs were mapped to the
terms of KEGG. We used KOBAS (Mao et al. 2005) software to
test the statistical enrichment of DEGs in KEGG pathway.
Quantitative real-time PCR (q-PCR)
Q-PCR
reaction system (10 μL per volume) consisted of 2 μL of cDNA, 0.5
μL of each primer (Table 1), 5 μL SYBR Premix Ex TaqTM II, and 1.5
μL of RNase-free ddH2O. The q-PCR
was reacted on Real-Time System (Bio-Rad, USA). The thermal cycling conditions were set at 5 min at 95ºC, then followed by 30 cycles of 94ºC for 30 s, 58ºC for 30 s and 72ºC for 30 s. finally, q-PCR ended with a melting curve
analysis program. Primers specificity was commanded by
dissociation curves and PCR products. The mean Ct values were normalized to the
acting gene (GAPDH, GenBank accession no. AY123769). The levels of gene
expression were calculated using 2-ΔΔCt method (Livak and
Schmittgen 2001).
Statistical analysis
Physiological indicators were estimated in a completely
randomized design with three replications. Statistical significance of
differences was calculated using SPSS 19.0, and differences among the
treatments were considered significant at p-value < 0.05.
Results
Changes in SSC, TA, TAC and TPC during fruit
ripening
The changes of fruit color, sugar
and acid are important characteristics of fruit maturation. At early stage, the
skin color of fruit was green, the titratable acid content was higher, the SSC
and TA content in the fruit between ‘Spartan’ and ‘Rake’ were almost equal, and
the anthocyanin and total phenol contents were very low (Fig. 1; Table 2). As
the fruit matured, the sugar and anthocyanin content in ‘Spartan’ fruit rapidly
accumulated, while the sugar content in ‘Rake’ fruit was relatively slower, and
titratable acid content showed an obvious decreasing trend. At this time, the
TPC in the fruit significantly increased.
Table 1: The primers of qRT-PCR reaction
Gene |
Forward sequence (5'-3') |
Reverse sequence (5'-3') |
PCR products (bp) |
114368_c1 |
AGCTGTTATGGCAGGTTTATGC |
GGTGGATCTGGACTGGAAGG |
137 |
97621_c0 |
CGATGCTAACGGTGTCTGGA |
TCGGATAGGTTGGCTGGATA |
129 |
99130_c0 |
CATTGCCGACTGCCTTGA |
TTACCCACTGCACCGCTTA |
147 |
108372_c0 |
GATGCACGCCGGATTAGC |
CGTTTGTCTTTCCCACCCA |
165 |
110081_c0 |
ACTTTGTCCCCACCGTCTCC |
GCCAGCATCCTCCCGAAC |
163 |
109295_c0 |
CAAAATCTTCCCTTTCACATCC |
CGTCCTTGCCCAGTCCAT |
179 |
107092_c0 |
GGTTTTGGAGACGAGGGG |
AATTGATATGAACTTGCGATGC |
104 |
110259_c0 |
GTTTCCCAGCGGAGTTCTTC |
GCCTCAACCATACGCCAGTA |
141 |
108040_c0 |
ACGAGATCCACGGCGACA |
CAGCAATGAACAAGCGAAGC |
172 |
107243_c0 |
GCTATTCCCTACTCGCTCACA |
CCTAGCCAGTCACTGCCTTTC |
126 |
114171_c2 |
AGGTTCGGTTGGATTGTGC |
TCGTTGCTATCCTCATTCTCG |
200 |
105148_c0 |
ACTCGGAAGGCCAAATAAGAA |
CAGTAGTGAGGGCAGTCGGT |
156 |
GAPDH |
TGTTGTGGGAGTCAATGAGAAA |
TGCGGTGATAGAGTGGATGG |
157 |
Table
2: Variations of physiological
characteristic in blueberry during fruit maturation
Sample |
Soluble solid content (%) |
Titratable acidity (%) |
Total anthocyanin (mg.100 g-1) |
Total polyphenol (mg.100 g-1) |
S1 |
3.30±0.01f |
1.32±0.01b |
21.95±0.05e |
335.16±0.03a |
S2 |
7.80±0.00c |
1.16±0.00c |
87.19±0.08d |
214.97±0.01e |
S3 |
14.33±0.03a |
0.35±0.06f |
241.17±0.08b |
283.26±1.00b |
R1 |
4.17±0.03e |
1.58±0.01a |
17.94±0.01f |
249.86±0.02d |
R2 |
6.43±0.00d |
0.91±0.01d |
101.94±0.02c |
173.73±0.03f |
R3 |
8.17±0.03b |
0.36±0.00e |
299.20±0.08a |
257.28±0.09c |
RNA-seq and de novo assembly
To ascertain metabolic and gene
expression changes during fruit ripening of different genotypes, transcriptome
analysis was performed. Six libraries prepared of two varieties at three
different development stages (Fig. 1). All of 40.54 Gb clean reads were
acquired from RNA-Seq after removing the reads with adaptors and low quality
reads (Table S1). Total of 109,480 unigenes and 223,382 transcripts were
received, that N50s were 1111 and 1604, respectively. This showed a high
quality assembly. Moreover, the total nucleotides of the unigenes and
transcripts were 74,760,274 and 214,156,807. The mean lengths of the unigenes
and transcripts were 683 and 959. Among the unigenes, 68,760 (62.81%)
unigenes had lengths of 200–500 nt, 20,635 (18.85%) unigenes had
lengths of 500–1000 nt, 12,817 (11.71%) unigenes had lengths ranging from 1000
to 2000 nt, and 7,268 (6.63%) had lengths of over than 2000 nt, to a mean
length of 683 (Table 3).
Functional annotation and classification
To obtained the possible functional information of all unigenes, a work of
sequence similarity search was executed in the NR, NT, KO, Swiss-Prot, Pfam, GO
and KOG databases. The results showed that 30,721 (28.05%), 18,340 (16.75%),
10,758 (9.86%), 22,528 (20.57%), 25,061 (22.89%), 25,414(23.21%)and 11,516 (10.51%) unigenes were
annotated in turn in the above seven databases. In conclusion, 4,022 unigenes
were annotated in all of seven databases, 37,880 unigenes were annotated in at
least one databases, with a threshold of 1.0E-5 (Table S2).
Based on the NR annotation, 6,492 (21.30%) of the unigenes appeared priority matches
with sequences of Vitis vinifera, and
only 1,687 (5.5%), 1,447 (4.8%) and 1,432 (4.7%) of the unigenes appeared
priority matches with sequences of Coffea
canephora, Theobroma cacao and Sesamum indicum, respectively (Fig. S1).
Based on the GO annotation, 25,414
unigenes were classified into 56 functional units
which pertained to three primary categories: biological process, cellular
component, and molecular function. Under the category of biological process,
cellular process (14,369 unigenes, 23.76%) and metabolic process (13,674
unigenes, 22.61%) dominated the primary proportion. For the category of
cellular component, large numbers of unigenes were categorized as cell (8,042
unigenes, 19.79%) and cell part (8,040 unigenes, 19.78%). About the category of
molecular function, binding (13,976 unigenes, 46.18%) and catalytic activity
(11,575 unigenes, 38.25%) were two main classification (Fig. S2).
Based on the KOG database, 11,516
unigenes were classified into 26 different functional groups (Fig. S3). A lot
of the unigenes were included in ‘General function prediction only’. But only
one ungene was clustered in ‘Unamed protein’ category.
Based on the KEGG database, all of
10,758 unigenes were mapped into 249 KEGG pathways (Fig. S4). This showed that
three largest pathways were carbohydrate metabolism (998 unigenes), translation
(897 unigenes) and folding, sorting and degradation (812 unigenes). Among those
metabolism categories, carbohydrate
metabolism represented the most predominant pathway, which have received most
attention with biosynthesis of other secondary metabolites in
connection with fruit taste, color and oxidation resistance. Those annotations
of gene were beneficial in identifying key genes in fruit ripening, and providing useful references for quality
breeding in blueberry.
Table 3: Length
distribution of all transcripts and unigenes
Transcript length |
Transcripts |
unigenes |
200–500bp |
100528(45.01%) |
68760(62.81%) |
500–1000bp |
50606(22.65%) |
20635(18.85%) |
1000–2000bp |
44404(19.88%) |
12817(11.71%) |
>2000bp |
27844(12.46%) |
7268(6.63%) |
Total |
223382 |
109480 |
Mean length |
959 |
683 |
N50 |
1604 |
1111 |
Total
nucleotides |
214156807 |
74760274 |
Fig.
1: Different stages of ‘Spartan’ and ‘Raka’
fruit ripening. TD: fruit transverse diameter (mm); LD: fruit longitudinal
diameter (mm)
DEG expression and function enrichment
To show the level of gene
expression, the read-count of gene was calculated and normalized to RPKM. Gene
differential expression analysis was performed between different varieties of
one development stage, and different development stages of same variety.
Comparing the two varieties, all of 2,021 DEGs were found, with 1,324 (703 up,
621 down), 756 (475 up, 281 down) and 741 (395 up, 346 down) in green fruit
stage, pink fruit stage and blue fruit stage, respectively (Fig. 2a).
Between three stages, 2,413 and 1,055 DEGs were found in ‘Spartan’ and ‘Raka’.
794 up and 672 down DEGs were observed between blue to green fruit stages in
‘Spartan’, and 456 up and 390 down DEGs were observed between blue to green
fruit stages in ‘Raka’ (Fig. 2b–d).
To further clarify these gene
expression patterns, we analyzed samples from three developmental stages in two
varieties. Overall, 69 up and 41 down DEGs in carbohydrate metabolic process
(GO: 0005975), 45 up and 29 down DEGs in single-organism carbohydrate metabolic
process (GO: 0044723), 71 up and 78 down DEGs in oxidation-reduction process
(GO: 0055114) were observed between R1 and S1. 51 up and 27 down DEGs in
oxidoreductase activity (GO: 0016491) was observed between R2 and S2. 52 up and
35 down u DEGs in oxidoreductase activity (GO: 0016491), 50 up and 33 down DEGs
in oxidoreductase activity (GO: 0016491), oxidation-reduction process (GO:
0055114), and 7 up and 6 down DEGs in antioxidant activity (GO: 0016209) were
observed between R3 and S3. During fruit development, a lot of DEGs in
carbohydrate were accumulated and antioxidant activity was improved in pink
stage. But most of DEGs in blue stages were down-regulated (Table S3).
The KEGG pathway enrichment
performed in all the DEGs. Comparing the two varieties, phenylalanine
metabolism was primary different pathway. For different development stages,
phenylalanine metabolism, flavonoid biosynthesis, starch and sucrose metabolism
and biosynthesis of amino acids were the most significant pathways between pink
stages with green stages. At blue with pink and green stages, flavonoid
biosynthesis, phenylalanine metabolism and starch and sucrose metabolism were
primary different pathway. Furthermore, we detected 9 and 14 DEGs in flavonoid
biosynthesis and starch and sucrose metabolism (Table S4).
Recognizing genes related to sugar and organic Acid
A total of 14 DEGs related to
sucrose metabolism were identified by RNA-Seq (Table S5). By comparison of two
varieties in three stages, we found that SPS (113824_c0) was up-regulated in
R1/S1 and down-regulated in R3/S3. NI (99130_c0) was up-regulated in R2/S2,
respectively. FK (102118_c0) were down-regulated in R2/S2 and R3/S3. SUT
(109295_c0) was down-regulated in R1/S1. We found most of genes during fruit
ripening were up-regulated in S3/S1, but SPS (113824_c0) and NI (99130_c0) were
down-regulated in R3/R2 and R3/R1 (Fig. 3a).
Genes of starch hydrolysis amy (108744_c0) and malZ (112107_c0) were up-regulated in R1/S1, R2/S2 and R3/S3, and
were up-regulated during fruit ripening. Gene of starch synthesis glgB
(112085_c0) was down regulated in S2/S1, and no significant difference in S3/S2
and S2/S1 (Fig. 3b). In glycolysis/gluconeogenesis pathway, most of genes were
up-regulated between pink stages with green stages in two varieties, but no
significant difference between blue stages with pink stages and blue stages
with pink stages. PFK (102118_c0) was up-regulated in R3/S3, PGM (111749_c0)
and PDC (106622_c1) were up-regulated in R2/S2. Obviously, PEPCK (111269_c0) was
up-regulated and PEPC (110823_c0) was down-regulated during fruit ripening (Fig. 3c).
Most of genes in TCA cycle and GABA
shut were up-regulated during fruit ripening, no significant difference between
two varieties. At the same
times, we pated attention to the genes CS
(110259_c0), IDH (109088_c1) and ME (96550_c0) were up-regulated in R3/R2, but
down-regulated in S3/S2. On the contrary, GAD (114171_c2) and GS (108803_c0)
were down-regulated in R3/R2, but up-regulated in S3/S2. In addition, CS
(110259_c0) was up-regulated and GS (108803_c0) was down-regulated in R3/S3
(Fig. 3d–e).
Recognizing genes related to flavonoid
All of the 18 genes differentially expressed unigenes were related to
flavonoid biosynthesis. By comparison two varieties in three stages (Table S5),
we found most of genes
Fig. 2: Changes in gene expression profile among six
samples. a–c. Vinn diagram showing the total number of DEGs between two
cultivars of three stages. d. The numbers of up-regulated and down-regulated
DEGs between each pair
were highly expressed in pink
stages, but were little reduced at blue stages. Among them, F3′H (109742_0)
and F3′5′H (111660_0) were up-regulated in R3/S3. PAL (100652_c1,
114146_c0 and 115162_c1), 4CL (111938_c0) and UFGT (111363_c0) were
down-regulated in R1/S1. But DFR (102889_c0) and FLS (109531_c0) were
up-regulated in R1/S1 and R2/S2 (Fig. 4).
Total of 1,165 Transcription
factors, including 58 MYB and 65 bHLH, were Identified from 109,480 unigenes of
RNA-Seq (Fig. S5). Heat map illustrating of transcription factors about MYB,
bHLH and WD40 during blueberry fruit development was constructed (Fig. S6). We
obtained 10, 8 and 9 unigenes of MYB, bHLH and WD40, respectively.
Verification of RNA-Seq results
To confirm the reliability of DEGs
profiles that identified from transcriptome sequencing analysis. We selected 12
candidate DEGs to q-PCR assays, all of them had a high expression levels in
most samples (Fig. 5). The results of q-PCR confirmed that the expression
patterns of these DEGs were consistent with the transcriptome sequencing
analysis results.
Discussion
Sweetness and antioxidant
resistance were the key quality traits of blueberry fruit. Majority of fresh
blueberry varieties on the market were north highbush blueberry. In this, some
varieties showed lower sweetness and higher antioxidant activity, but the
others showed higher sweetness and lower antioxidant activity. Today, we still
poorly understood about the mechanism of sugars accumulation, organic acid metabolism
and antioxidant formation in blueberry fruits.
In this experiment, we performed
RNA-Seq to blueberry fruits at three developmental stages between two
varieties, which indicated significant differences in SSC, TA, TAC and TPC
between two varieties in maturity. Some genes and transcription factors related to sugars and organic acid
metabolism and anthocyanin biosynthesis were found from transcriptome
sequencing results. The analysis of the differential expressions of these genes
in different varieties and stages of development showed that SPS and NI were
key genes for sugar accumulation, ACO, NADP-IDH, GAD, GABA-AT and GS
co-regulated organic acid metabolism, and F3'H, F3'5'H, FLS and UFGT were key enzymes regulating the
biosynthesis of blueberry flavonoids.
Fig. 3: Schematic of metabolic data related to blueberry fruit
sugar and citrate during ripening
Fig.
4: Schematic of metabolic data related to blueberry
fruit flavonoid during ripening. a. speculated pathway of flavonoid
biosynthesis in blueberry. b. the pattern of gene expression
In most fruit trees, photosynthetic
products are mainly sucrose, which is transported to fruit through phloem (Zhen et al. 2018). Sugars are
constantly accumulated during fruit development and maturation. However,
different fruit types had different patterns of sugar accumulation, and the
effect of enzyme genes on sugar accumulation was also different. SPS dominates
sucrose synthesis in strawberry, peach and kiwi fruit (Hubbard et al. 1991), especially, an
increase in SS activity during fruit ripening played a major role in
sucrose accumulation of peach fruit (Etienne
et al. 2002; Desnoues et al. 2014).
In addition, in apples (Tong et al.
2018), grapes (Xin et al. 2013),
litchi (Yang et al. 2013), pears
(Vizzolo et al. 2003; Yao et al. 2010) and other fruits, hexose
accumulation played a major role for fruit maturity. During fruit ripening, the
activity of Inv increased and
catalyzed sucrose to hydrolyze into fructose and glucose. Blueberry was a
typical hexose accumulative fruit (Ayaz et
al. 2001). Between two different varieties we tested, AI was up regulated
throughout all ripening stages in both varieties, while NI and SPS were up
regulated in the sweeter variety ‘Spatan’ and down regulated in ‘Raka’ at the
fruit maturation stage. However, SS was not differentially expressed between
two varieties in fruit maturation. We further speculated that SPS and NI
express associated with sugar accumulation during the ripening of the blueberry
fruit. Other studies have shown that there was no correlation between Inv and sugar accumulation in grape (Davies and Robinson 1996). At the same
time, both AI and NI regulated sugar accumulation in the same way in pear
(Zhang et al. 2014). In addition, we
also identified a SUT, which showed significant difference only in the two
varieties in the green fruit stage, but no difference was
noted at different stages of development. The specific relationship between
this transporter and sugar accumulation needs verification.
Fig. 5: qRT-PCR validation of 12 DEGs of ‘Raka’ and ‘Spatan’
during three fruit development stages
The metabolic process of organic
acid is very complicated in fruit. There are many reasons for the decrease of
organic acid content in mature fruits, including increase in fruit volume,
water content, greater decomposition of organic acid than synthesis, while
organic acid plays a role in respiration and gluconeogenesis as a substrate
(Sadka et al. 2003). In citrus (Cercos
et al. 2006), citrate metabolism was
connected with acetyl-CoA pathways and GABA shunt. PEPC and CS were main
enzymes regulating the synthesis of citric acid. In the studies of melon and
grape (Diakou et al. 2000; Tang et al. 2010) during different
development stages, it was agreed that fruit malic acid accumulation was
related to PEPC activity, while CS catalyzed oxaloacetic acid and acetyl-CoA to
condense into citric acid, and acetyl-CoA was generated. During the development
of loquat (Chen et al. 2009), changes
in CS and PEPC were positively correlated with changes of TA content. However,
other studies have suggested that there is no absolute correlation between CS
activity and the levels of citric acid accumulation (Sadka et al. 2001). In addition, in the development of lemon (Sadka et al. 2003), the decrease of ACO
activity in the mitochondrial promoted organic acids accumulation in the early
stage of fruit development, and the increase of ACO activity in the cytoplasm
decreased the level of organic acids in the fruit maturation. At early stage of
fruit development, NADP-IDH activity of mitochondria decreased, which promoted
citric acid synthesis and inhibited ACO activity, leading to weakened TCA
cycle. Therefore, it is believed that citric acid accumulation inhibited the
activity of ACO and NADP-IDH in the TCA cycle. In this study, the PEPC activity
of both varieties showed a downward trend with fruit ripening, and there was no
significant difference between varieties. However, CS activity increased with
fruit ripening in ‘Raka’, and slightly increased first and then decreased in
‘Spatan’. It can be seen that the accumulation of citric acid in fruits is
related to the activity of CS.
The expression pattern of NADP-IDH
was the same with ACO in the two varieties. NADP-IDH was up regulated with
fruit ripening in ‘Raka’, and slightly decreased with fruit ripening in
‘Spatan’. In the GABA pathway, GAD, GABA-AT and GS were up-regulated with
ripening in the fruit, up-regulated first in ‘Raka’ and then down-regulated in
ripening fruit. Therefore, we believe that GABA pathway is the main pathway of
citric acid metabolism in blueberry fruits, and ACO, NADP-IDH, GAD, GABA-AT and
GS jointly regulated citric acid metabolism. In addition, ME played an
important role in regulating the synthesis and degradation of fruit malic acid.
We found that the expression of ME showed an opposite trend during the ripening
process of the two blueberry varieties, rising gradually with fruit ripening in
‘Raka’ and decreasing gradually in ‘Spatan’. In grapes, increased ME activity
promoted malic acid production (Sweetman et
al. 2009), while in apples, pears and plum, increased ME activity in the
cytoplasm promoted malic acid degradation (Berüter 2004; Mu et al. 2018). The specific role of ME in
malic acid metabolism of blueberry remains to be further studied.
As the most widely
studied pathway in plants, many structural genes and regulators in flavonoid
biosynthesis have been cloned from Arabidopsis
thaliana, Zea mays, petunia and
other model plants (Lepiniec et al.
2006). In blueberry fruits, flavonoids mainly included flavonols, anthocyanins
and procyanidins (Prior et al. 2001).
Studies have shown that proanthocyanidins were mainly synthesized with a high
expression in F3'H and a low expression in F3'5'H in the early stage of
blueberry fruit development. When fruit matures, the concentration of
proanthocyanidins decreases, and F3'H and F3'5'H increases (Zifkin et al. 2012). In apples, high
concentration of PA was the result of high expression of F3'H gene and obvious
deletion of F3'5'H gene (Han et al.
2010). The high concentration of PA was mainly related to the high expression
of F3'5'H in persimmon (Akagi et al. 2009).
In addition, proanthocyanidin biosynthetic genes ANR and LAR were expressed in
early stage of fruit development, which were significantly separated from
anthocyanin specific genes DFR, ANS and UFGT in time (Prior et al. 2001). In grape seeds, the expressions of F3'H, DFR and LAR
were significantly increased at the initial stage of ripening, while ANR and
ANS were also highly expressed (Bogs et
al. 2005; 2006). However, this does not seem to be the case with
blueberries. In this experiment, we found that the expression levels of F3'H
and F3'5'H in the two varieties increased gradually during fruit ripening, and
the expression of F3'H in the three stages of development was lower than
F3'5'H. In the ripening fruits, the expression level of ‘Raka’ was
significantly up regulated in ‘Spatan’. However, there was no significant
difference in the expression about proanthocyanidin synthesis genes ANR and LAR
in different varieties and stages, which may be related to the decreased
synthesis of proanthocyanidin in fruit ripening stage. In addition, FLS expression
of flavonol synthesis gene was significantly higher than that of ‘Spatan’ in
the pink fruit stage, and there was no significant difference between the two
varieties in the late maturation stage. We speculated that flavonol and
procyanidins were accumulated in the early stage of fruit development, and the
concentration showed a decreasing trend as the fruit matured. In grapes and
blueberries, anthocyanins synthesis occurs rapidly from the ripening stage,
accompanied by rapid accumulation of UFGT in the peel (Jaakola et al. 2002). In this study, the
expression levels of anthocyanin synthesis related genes DFR, ANS and UFGT were
up regulated in both varieties, but there was no difference between varieties.
In the whole flavonoids synthesis pathway, only CHS, F3'5'H and FLS were
significantly higher than ‘Spatan’ in ‘Raka’ at maturity.
Therefore, we hypothesized that the difference in flavonoid content between
varieties was caused by the coordinated expression of the three genes. Studies
have also shown that FLS of flavonol synthesis gene and DFR of anthocyanin
synthesis gene inhibit each other (Luo et
al. 2016), which may be caused by the competitive substrates in the
synthesis of different flavonoids, and the specific mechanism needs further
experimental exploration.
Conclusion
To identify taste and oxidation
resistance expression patterns during fruit ripening, a RNA-Seq data analysis
between two varieties in three stages of fruit ripening was performed. We
obtained the most related genes focusing on carbohydrate metabolism and
flavonoid biosynthesis. Furthermore, gene expression and real-time
quantification analysis access to the two key genes related to sugar
accumulation, five key genes connected with organic acid metabolism and two key
genes connected with anthocyanin accumulation. This indicated a strong
involvement of anthocyanins in the fruit quality. This provided a new way to
understand the molecular mechanisms underlying fruit quality formation in
blueberry.
Acknowledgements
Thank to Dr. Youchun Liu and Dr.
Xin Wei for their help in the experiment and Dr. zhengxin zhu for his technical
support in data analysis.
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