Abstract
In Chinese cabbage breeding, hybrids have made a terrific contribution
due to heterosis, the superior performance of offspring compared to
their inbred parents. Since the development of new, top-performing
hybrids requires a large scale of human and material resources, the
prediction of hybrid performance is of utmost interest to plant
breeders. In our research, leaf transcriptome data from eight parents
were used to investigate if they might be employed as markers to
predict hybrid performance and heterosis. In Chinese cabbage, heterosis
of plant growth weight (PGW) and heterosis of head weight (HW) were
more obvious than other traits. The number of differential expression
genes (DEGs) between parents was related to the PGW, length of the
biggest outer leaf (LOL), leaf head height (LHH), leaf head width
(LHW), HW, leaf number of head (LNH) and plant height (PH) of hybrids,
and up-regulated DEGs number was also associated with these traits.
Euclidean and binary distances of parental gene expression levels were
significantly correlated with the PGW, LOL, LHH, LHW, HW and PH of
hybrids. Additionally, there was a significant correlation between the
parental expression levels of multiple genes involved in the ribosomal
metabolic pathway and hybrid observations and heterosis in PGW, with
the BrRPL23A gene showing the highest correlation with the MPH of PGW(r
= 0.75). Therefore, leaf transcriptome data can preliminarily predict
the hybrid performance and select parents in Chinese cabbage.
Keywords: heterosis, differential expression genes, Euclidean distance,
binary distance
1. Introduction
Heterosis is a phenomenon in which hybrids outperform their homozygous
parents in vitality, growth vigor, fecundity, yield, quality, stress
resistance and adaptability [[30]1,[31]2,[32]3,[33]4,[34]5]. As a
common biological phenomenon, heterosis can be observed in almost all
sexually reproducing species, from plants to animals, and even in
microorganisms. Firstly, Darwin systematically studied heterosis as a
result of the hybridization of organisms with different genetic
components and further proposed that heterozygous pollination is
beneficial to plants while self-pollination is detrimental. Then, the
dominant hypothesis [[35]6], over-dominance [[36]7,[37]8,[38]9],
epistatic effects [[39]10,[40]11] and other hypotheses were proposed to
explain the formation mechanism of heterosis. Since its introduction,
heterosis has emerged as the primary method for increasing the yield of
grain [[41]12,[42]13], oil crops [[43]14] cotton [[44]15,[45]16] and
vegetables [[46]17,[47]18]. Meanwhile, the use of heterosis can also
improve the stress resistance and adaptability of crops. Heterosis has
contributed significantly to global food production, brought about
enormous economic and social advantages, and is also a prominent
achievement of modern agricultural biotechnology [[48]19,[49]20].
The identification of new superior hybrids among a large number of
possible crosses in new parental lines generated each year requires
extensive testing programs, including the production of numerous test
crosses, extensive multi-location/-year field trials to generate
phenotypic data and to test hybrid performance [[50]21]. Therefore,
using data collected from parental inbred lines to predict the
performance of hybrids promises to improve the efficiency of
cross-breeding and is of great interest to breeders. Currently, field
data, DNA markers, whole-genome data, transcriptome data and so on, are
used to predict the performance of hybrids and to analyze the
relationship between various characteristics of parents and heterosis
in hybrids for improving breeding efficiency [[51]22,[52]23,[53]24].
With further research on heterosis, genetic distance has been used as a
measure of the degree of genetic differences between parents to select
parents and predict hybrid performance. In general, genetic differences
between parents are greater and hybrid offspring have the more obvious
hybrid heterosis, but this does not imply that this relationship will
be across the whole range of species diversity [[54]25,[55]26,[56]27].
The hump quadratic polynomial function was found between the genetic
distance of parents and the phenotype of hybrids. Within a certain
range, heterosis raises with increasing genetic distance between
parents, but beyond this range, heterosis tends to decrease with
increasing genetic distance [[57]28]. In addition, some studies
indicated that heterosis was significantly correlated with the genetic
distance between parents [[58]29,[59]30,[60]31] or had no obvious
relationship [[61]32]. Investigating the relationship between parental
genetic distance and hybrid phenotypic traits is an essential
combination of molecular genetics and conventional breeding [[62]33].
Therefore, the study on the relationship between parental genetic
distance and heterosis is crucial for the effective prediction of
heterosis, scientific guidance on parental selection and rational use
of heterosis.
Chinese cabbage (Brassica rapa L. ssp. pekinensis), which originated in
China, is one of the largest and most productive vegetable crops grown
in China. It is highly consumed in Asian countries and is one of the
most important vegetable crops in the world [[63]34,[64]35,[65]36]. In
Chinese cabbage, heterosis is evident and employed as an effective way
and important means to improve yield, disease resistance, stress
resistance and quality. However, there are few studies about predicting
the performance of Chinese cabbage hybrids. Thus, there is a need to
find a method for selecting parents and predicting the performance of
hybrids in Chinese cabbage.
In this study, eight inbred lines and 53 hybrids were used as plant
materials to explore whether parental transcriptome data could be used
to predict hybrid performance and select parents. Firstly, the
correlation between the parental number of DEGs and hybrid performance
was calculated. Secondly, the correlation between the parental genetic
distance based on transcriptome data and hybrid performance was
counted. Finally, the gene-related heterosis was identified by
analyzing the correlation between parental expression level and the
performance of hybrids. These analyses are conducive to the application
of transcriptome data in heterosis prediction, and also provide a
reference for heterosis prediction in breeding process of Chinese
cabbage.
2. Materials and Methods
2.1. Plant Materials
Eight inbred lines and 53 hybrids were used for heterosis analysis
([66]Table 1). All 8 Chinese cabbage inbred lines were developed and
provided by the Chinese cabbage research group, at the College of
Horticulture, Northwest A&F University, Yangling, China, which were
self-bred for at least eight generations. Appling complete diallel
crossing design, the inbred line parents of Chinese cabbage were used
for artificial cross-pollination to obtain the hybrids. The details of
the crosses were presented in [67]Table 1. In all the materials, inbred
lines A, B, C, D, E, F, G, and H were parents, and the other materials
were hybrids.
Table 1.
The codes of inbred lines and hybrids of Chinese cabbage.
Male Parent A B C D E F G H
Female Parent
A AB / AD AE AF AG AH
B BA BC BD BE BF BG BH
C CA CB CD CE CF CG CH
D DA DB DC DE DF DG DH
E EA EB EC ED EF EG EH
F FA FB FC FD FE / FH
G / GB GC GD GE GF GH
H HA HB HC HD HE HF HG
[68]Open in a new tab
The code in the first column represents the female parent, the code in
the first line represents the male parent, and the rests are the
corresponding hybrids. /: The material is missing.
The parents and hybrids were cultured in the same experimental field at
the Yangling Wuquan test field in Shaanxi, China. At the middle heading
stage (about 70 days), the first outer leaf of the Chinese cabbage
parents was collected from top to bottom as an RNA-Sep sample. Three
individual plants were mixed as a test sample, and three replicates
were selected for each material. At the maturity stage(about 100 days),
parents and hybrids were investigated for yield traits and yield
related traits including plant growth weight(PGW)(data from our
previous project) [[69]37], head weight(HW), plant width (PW), PH,
number of outer leaves(NOL), LOL, width of the biggest outer leaf(WOL),
leaf head height(LHH), leaf head width(LHW), and leaf number of
head(LNH).
2.2. Heterosis Statistical Analysis
Data collected from the field were used to analyze the heterosis of
traits. The heterosis require the calculation of mid-parent heterosis
(MPH) and high-parent heterosis (HPH). The formulae for their
calculation are as follows:
[MATH:
MPH =<
mrow>F1−MP
MP×100% :MATH]
(1)
[MATH:
HPH =<
mrow>F1−HP
HP×100% :MATH]
(2)
where F[1] is the value of hybrid, MP is the mean value of two parents,
and HP is the value of the better parent.
2.3. RNA Extraction, Library Construction and RNA-Seq
Total RNA was extracted using the Trizol reagent following the
manufacturer’s instructions by Genedenovo Biotechnology Co., Ltd.
(Guangzhou, China). The RNA quality and concentration were examined
using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara,
CA, USA). The mRNA was isolated using magnetic beads with Oligo (dT)
and fragmented into small pieces using fragmentation buffer. Then, the
mRNA fragments were used as templates to synthesize the first strand of
cDNA with random hex base random primers and the second chain of cDNA
with buffer, dNTPs, RNase H and DNA polymerase I. The synthesized cDNA
were purified using a QiaQuick PCR extraction kit and subjected to end
reparation and single nucleotide A (adenine) addition. Thereafter, the
short fragments were ligated to Illumina sequencing adapters and the
suitable sized fragments were selected as templates for PCR
amplification. Finally, the transcriptome libraries were sequenced
using Illumina HiSeq™2500 by Genedenovo Biotechnology Co., Ltd.
(Guangzhou, China). The obtained raw data from constructed cDNA
libraries were deposited in NCBI Sequence Read Archive (SRA,
[70]http://www.ncbi.nlm.nih.gov/Traces/sra/ accessed on 1 June 2022)
under the accessionnumber BioProject PRJNA876066 [[71]37].
2.4. Differentially Expressed Genes Analysis
Raw reads from RNA-seq were obtained from our previous project
[[72]37], and then mapped to the Chinese cabbage genome sequences from
the Brassica database ([73]http://brassicadb.org/brad) using TopHat2
software [[74]38]. Gene expression levels were normalized using the
fragments per kilobase of transcript sequence per millions (FPKM)
method. Differentially expressed genes between groups were analyzed
using Edge software. The FDR < 0.05 and |log2^FC| > 1 were used as the
threshold to identify significant DEGs.
2.5. Transcriptome-Based Distance Analysis
Euclidean and binary distances were employed as indicators to measure
the parental differences and were calculated by parental transcriptome
data. The expression level of all genes was calculated, and then
Euclidean and binary distances between parents were calculated based on
the R language.
2.6. Identifying Genes Correlated to PGW and MPH
The mean, maximum and minimum valuess of parental gene expression level
were calculated as mid-parent expression val high-parent expressionion
value, and low-parent expression value, respectively. Correlation
coefficients of high-parent expression value, mid-parent expression
value, and low-parent expression value with measured value and MPH of
PGW in hybrids were calculated. Pearson’s product–moment correlation in
R was used to test the significance of the correlation coefficients.
For multiple testing corrections, p values were adjusted with a false
discovery rate of 0.01.
2.7. GO and KEGG Enrichment Analysis
To identify possible biological functions of DEGs, Gene Ontology (GO)
and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses
were performed. The DEGs were mapped to terms in the GO database
([75]http://www.geneontology.org/). Then, significantly enriched GO
terms were searched by comparing them to the genome background with an
adjusted p-value ≤ 0.05 as the threshold. KOBAS software was used for
pathway enrichment analysis based on the KEGG database [[76]39]. A
corrected p value ≤ 0.05 was used as the threshold to identify the
significantly enriched functional terms and pathways.
3. Results
3.1. Statistical Analysis of 10 Traits in Hybrids and Parents of Chinese
Cabbage
By analyzing the traits of parents and hybrids, it was found that the
hybrids had higher means in these traits, including PGW, NOL, LOL, WOL,
LHH, LHW, HW, LNH, PW and PH, compared to the parents ([77]Table 2).
For instance, the mean of hybrids is 3.75 kg, ranging from 1.40 kg to
6.50 kg, whereas the mean of the parents for PGW is 2.13 kg, ranging
from 0.53 kg to 3.53 kg. The PGW mean in hybrids was higher than that
in the parents, and other traits followed a similar pattern to the PGW.
Table 2.
Analysis of phenotype variation of traits for parental inbred lines and
hybrids.
Trait Parents Hybrids
Mean Value Range of
Variation Variance
Coefficient Mean Value Range of
Variation Variance
Coefficient
PGW 2.13 ± 0.96 0.53–3.53 44.95 3.75 ± 0.99 1.40–6.50 26.33
NOL 8.63 ± 2.50 5.00–11.67 29.03 9.72 ± 2.05 6.33–17.00 21.13
LOL 38.36 ± 8.95 23.50–51.33 23.33 44.47 ± 5.59 32.67–56.00 12.57
WOL 24.40 ± 5.91 15.63–34.00 24.21 30.37 ± 3.76 22.00–37.50 12.39
LHH 27.25 ± 6.58 19.33–37.50 24.16 29.62 ± 5.13 19.00–42.14 17.32
LHW 17.76 ± 2.31 13.40–21.50 13.02 21.19 ± 2.29 13.00–26.50 10.83
HW 1.39 ± 0.65 0.46–2.49 46.59 2.57 ± 2.29 0.52–4.22 26.34
LNH 34.92 ± 4.82 27.67–41.33 13.81 39.14 ± 5.89 23.00–51.33 15.06
PW 54.63 ± 12.06 34.00–66.33 22.08 65.91 ± 8.32 45.50–93.67 12.62
PH 37.08 ± 6.91 29.33–49.00 18.64 39.25 ± 5.46 28.00–52.33 13.91
[78]Open in a new tab
PGW: plant growth weight, NOL: number of outer leaves, LOL: length of
the biggest outer leaf, WOL: width of the biggest outer leaf, LHH: leaf
head height, LHW: leaf head width, HW: head weight, LNH: leaf number of
head, PW: plant width, PH: plant height.
3.2. Heterosis of 10 Traits in Hybrids of Chinese Cabbage
Among the 53 hybrids, the mean MPH was higher for PGW and HW compared
to other traits ([79]Figure 1a). The MPH for PGW was 192.54, ranging
from 44.49 to 411.90, while the MPH for HW was 201.78, with a range of
28.40 to 444.02. The average MPH of NOL, LOL, WOL, LHH, LHW, LNH, PW
and PH were 115.03, 117.62, 127.10, 109.58, 95.17, 112.08, 122.90 and
106.83, respectively. For these traits, HPH had a similar pattern to
MPH. Compared to other traits, PGW and HW had higher average HPH
([80]Figure 1b). The average HPH for PGW and HW were 149.49 and 160.37,
respectively. The average HPH for NOL, LOL, WOL, LHH, LHW, LNH, PW and
PH was 98.64, 103.38, 111.10, 96.39, 111.53, 103.50, 108.43 and 96.89,
respectively.
Figure 1.
[81]Figure 1
[82]Open in a new tab
The mid-parent heterosis value and high-parent heterosis value in
hybrids. (a) The mid-parent heterosis value of different traits in 53
hybrids. (b) The high-parent heterosis value of different traits in 53
hybrids. (c) The mid-parent heterosis value of plant growth weight with
different parents. (d) The high-parent heterosis value of plant growth
weight with different parents. PGWplant growthth weight, NOL: number of
outer leaves, LOL: length of the biggest outer leaf, WOL: width of the
biggest outer leaf, LHH: leaf head height, LHW: leaf head width, HW:
head weight, LNH: leaf number of head, PW: plant width, PH: plant
height. A, B, C, D, E, F, G and H were parent inbred lines.
The heterosis of hybrids varied among parents. For PGW, the average MPH
of hybrids was higher than that of hybrids crossed with other parents
when the inbred lines C, G and H were used as parents ([83]Figure 1c).
When inbred lines C, G and H were used as parents, the averages MPH of
hybrids were 249.37, 247.20 and 219.78, respectively. When inbred lines
E and F were used as parents, the average MPH of hybrids was lower than
that of hybrids from other parental crosses, with the averages of
122.61 and 150.73. Among the eight parents, the hybrids from parents G
and H had higher HPH, with the average of 196.41 and 176.16 ([84]Figure
1d). The hybrids from parents E and F had lower HPH compared to other
parents with mean values of 92.54 and 121.27, respectively. Overall,
compared with other materials, parent G and H’s hybrids had more
obvious heterosis, while parent E and F’ s hybrids had lower heterosis
in PGW.
3.3. Correlation between the Parental DEGs Number and Hybrid Heterosis
To investigate the parental influence on heterosis, the correlations
between the number of parental DEGs and heterosis of different traits
in hybrids were analyzed. The results revealed that there was a
significant correlation between the number of parental DEGs and the MPH
of LNH (r = 0.30), but there was no significant correlation between the
number of parental DEGs and the MPH of other traits ([85]Table 3). The
number of up-regulated DEGs (female parent vs. male parent) was only
significantly correlated with the MPH of LNH (r = 0.51).
Table 3.
The correlation between the number of parental differential expression
genes and the mid-parent heterosis value of traits in hybrids.
Traits The Number of Genes
UP DOWN ALL
PGW 0.091 0.054 0.096
NOL −0.152 −0.134 −0.189
LOL 0.018 −0.076 −0.037
WOL 0.051 −0.109 −0.035
LHH 0.047 0.001 0.032
LHW −0.121 −0.117 −0.157
HW 0.133 0.079 0.141
LNH 0.508 ** −0.074 0.298 *
PW 0 −0.014 −0.009
PH 0.155 −0.048 0.075
[86]Open in a new tab
**: Correlation is significant at the 0.01 level, *: Correlation is
significant at the 0.05 level. PGW: plant growth weight, NOL: number of
outer leaves, LOL: length of the biggest outer leaf, WOL: width of the
biggest outer leaf, LHH: leaf head height, LHW: leaf head width, HW:
head weight, LNH: leaf number of head, PW: plant width, PH: plant
height. UP: up-regulated DEGs (female parent vs. male parent), DOWN:
down-regulated genes (female parent vs. male parent), ALL: DEGs (female
parent vs. male parent).
Analysis of the correlation between parental DEGs numbers and hybrid
traits revealed that parental DEGs number was significantly correlated
with several traits, including PGW, LOL, LHH, LHW, HW, LNH and PH
([87]Table 4). The number of up-regulated DEGs (female parent vs. male
parent) was significantly related to the observed value of PGW, LOL,
WOL, LHH, LHW, HW, LNH and PH. However, the number of down-regulated
genes (female parent vs. male parent) was not correlated with the
observed value of all traits.
Table 4.
The correlation between the number of parental differentiall expression
genes and observed value of traits in hybrids.
Traits The Number of Genes
UP DOWN ALL
PGW 0.340 * 0.104 0.298 *
NOL 0.028 0.013 0.027
LOL 0.437 ** 0.252 0.459 **
WOL 0.298 * −0.017 0.192
LHH 0.350 * 0.254 0.401 **
LHW 0.327 * 0.234 0.372 **
HW 0.354 ** 0.109 0.311 *
LNH 0.556 ** 0.002 0.379 **
PW 0.255 0.132 0.259
PH 0.437 ** 0.153 0.395 **
[88]Open in a new tab
**: Correlation is significant at the 0.01 level, *: Correlation is
significant at the 0.05 level. PGW: plant growth weight, NOL: number of
outer leaves, LOL: length of the biggest outer leaf, WOL: width of the
biggest outer leaf, LHH: leaf head height, LHW: leaf head width, HW:
head weight, LNH: leaf number of head, PW: plant width, PH: plant
height. UP: up-regulated DEGs (female parent vs. male parent), DOWN:
down regulated genes (female parent vs. male parent), ALL: DEGs (female
parent vs. male parent).
3.4. Correlation between the Transcriptome-Based Distances and Heterosis with
Traits
To explain the effect of parental differences on hybrid heterosis, the
correlation between parental genetic distance and hybrid heterosis for
different traits was analyzed. The results suggested that the Euclidean
distance between parents was significantly correlated with the hybrid
MPH of PGW, LHH, HW, LNH and PH. The binary distance between parents
was only significantly correlated with the MPH of LNH, but not with
other traits ([89]Table 5).
Table 5.
Correlation between mid-parent heterosis value of traits and parents’
genetic distance.
Binary Euclidean
PGW 0.109 0.347 *
NOL −0.191 0.082
LOL −0.029 0.245
WOL −0.025 0.207
LHH 0.041 0.449 **
LHW −0.147 0.15
HW 0.154 0.398 **
LNH 0.298 * 0.337 *
PW 0.001 0.189
PH 0.081 0.301 *
[90]Open in a new tab
**: Correlation is significant at the 0.01 level, *: Correlation is
significant at the 0.05 level. PGW: plant growth weight, NOL: number of
outer leaves, LOL: length of the biggest outer leaf, WOL: width of the
biggest outer leaf, LHH: leaf head height, LHW: leaf head width, HW:
head weight, LNH: leaf number of head, PW: plant width, PH: plant
height.
The Euclidean distance between parents was significantly correlated
with the observed value of PGW, LOL, LHH, LHW, HW, PW and PH. The
binary distance between parents was significantly correlated to
observed value of PGW, LOL, LHH, LHW, HW, LNH and PH ([91]Table 6). The
correlation coefficients of traits with Euclidean distances were larger
than binary distances, except for LNH.
Table 6.
Correlation between observed value of traits and parents’ genetic
distance.
Binary Euclidean
PGW 0.301 * 0.384 **
NOL 0.02 0.095
LOL 0.461 ** 0.559 **
WOL 0.196 0.162
LHH 0.404 ** 0.566 **
LHW 0.378 ** 0.393 **
HW 0.316 * 0.394 **
LNH 0.379 ** 0.263
PW 0.258 0.352 **
PH 0.399 ** 0.401 **
[92]Open in a new tab
**: Correlation is significant at the 0.01 level, *: Correlation is
significant at the 0.05 level. PGW: plant growth weight, NOL: number of
outer leaves, LOL: length of the biggest outer leaf, WOL: width of the
biggest outer leaf, LHH: leaf head height, LHW: leaf head width, HW:
head weight, LNH: leaf number of head, PW: plant width, PH: plant
height.
3.5. Genes Correlated to PGW and MPH
Genes associated with PGW were identified by analyzing the correlation
between parental gene expression levels and PGW in hybrids. In a total
of 993 genes, 1335 genes and 1003 genes, the high-parent expression,
mid-parent expression and low-parent expression were significantly
correlated with the PGW in hybrids ([93]Figure 2a). In addition, the
correlation between the parental gene expression level and the MPH of
PGW was analyzed. There were 5126 genes, 5439 genes and 4022 genes, in
which the high-parent expression, mid-parent expression and low-parent
expression were significantly correlated with the MPH of PGW of the
hybrids, respectively ([94]Figure 2b).
Figure 2.
[95]Figure 2
[96]Open in a new tab
Number of genes correlated to observed value and mid-parent heterosis
value of plant growth weight. (a) Number of genes correlated to plant
growth weight. (b) Number of genes correlated to mid-parent heterosis
value of plant growth weight. (c) Number of genes correlated to
observed value and mid-parent heterosis value of plant growth weight.
(d) Number of genes positively correlated to observed value and
mid-parent heterosis value of plant growth weight. (e) Number of genes
negatively correlated to observed value and mid-parent heterosis value
of plant growth weight. HPV: high-parent expression. MPV: mid-parent
heterosis value: mid-parent expression. LPV: low-parent expressionn.
Among these genes, 1084 genes were significantly associated with the
observed value and MPH of PGW ([97]Figure 2c). Among these genes, 567
genes were significantly positively correlated with the measurements
and MPH of PGW ([98]Figure 2d), and 457 genes were negatively related
to both the measured value and the MPH of PGW ([99]Figure 2e).
3.6. Enrichment Analysis of Genes Related to Heterosis
GO enrichment analysis was used to determine the function of 567 genes
whose parental gene expression levels were positively correlated with
the hybrid observed value and MPH of PGW. In the biological process,
these genes were significantly enriched in the regulation of autophagy
(GO: 0010506), regulation of embryonic development (GO: 0045995) and
regulation of cellular catabolic process (GO: 0031329) ([100]Figure
3a). In the molecular functions, these genes were significantly
enriched in phosphatidylinositol kinase activity (GO: 0052742),
receptor serine/threonine kinase binding (GO: 0033612) and structural
molecular activity (GO: 0005198). In the cellular components, these
genes were significantly enriched in large ribosomal subunit (GO:
0015934). A total of 457 genes, whose parental gene expression levels
were negatively related to hybrid observed value and MPH of PGW, were
significantly enriched in the molecular functional classification,
including ion binding (GO: 0043167), deoxyribonuclease activity (GO:
0004536) and cation binding (GO: 0043169) ([101]Figure 3b).
Figure 3.
[102]Figure 3
[103]Open in a new tab
Enrichment analysis of genes correlated to plant growth weight. (a) GO
enrichment analysis of genes positively correlated to observed value
and mid-parent heterosis value of plant growth weight in hybrids. (b)
GO enrichment analysis of genes negatively correlated to observed value
and mid-parent heterosis valueof plant growth weight in hybrids. (c)
KEGG enrichment analysis of genes positively correlated to observed
value and mid-parent heterosis value of plant growth weight in hybrids.
(d) KEGG enrichment analysis of genes negatively correlated to observed
value and mid-parent heterosis value of plant growth weight in hybrids.
KEGG pathway enrichment analysis indicated that the genes, whose
parental gene expression levels were positively related to hybrid
observed value and MPH of PGW, were significantly enriched in ribosome
(ko03010), nitrogen metabolism (ko00910) and alanine, aspartate and
glutamic acid metabolism (ko00250) ([104]Figure 3c). The most dominant
pathways were ribosome (ko03010). These genes, which parental gene
expression levels were negatively related to hybrid observed value and
MPH of PGW, were significantly enriched in terpenoid backbone
biosynthesis (ko00900), sulfur relay system (ko04122), vitamin B6
metabolism (ko00750), fatty acid elongation (ko00062), biosynthesis of
antibiotics (ko01130) and histidine metabolism (ko00340) ([105]Figure
3d). The most dominant pathways were terpenoid backbone biosynthesis
(ko00900).
3.7. Metabolic Pathway Related to Heterosis
In the ribosome metabolic pathway, the parental gene expression levels
of 17 genes were correlated with hybrid PGW, with a correlation
coefficient ranging from 0.28 to 0.43, and related to hybrid MPH of
PGW, with a correlation coefficient ranging from 0.31 to 0.75. Among
these genes, the parental gene expression levels of BraA03g010340.3C
(BrRPL10AC) showed the highest correlation with hybrid PGW (r = 0.43),
and the parental gene expression levels of BraA03g020910.3C (BrRPL23A)
had the highest correlation with hybrid MPH of PGW (r = 0.73)
([106]Table 7).
Table 7.
Correlation between parental gene expression levels in ribosome
metabolic pathwayand plantgrowth weight in hybrids.
GeneID Symbol PGW MPH of PGW
cor Q Value cor Q Value
BraA01g015640.3C RPL7AB 0.3184 0.0202 0.351 0.01
BraA02g038190.3C RPP2C 0.3917 0.0037 0.378 0.0053
BraA03g010340.3C RPL10AC 0.4304 0.0013 0.4291 0.0013
BraA03g020910.3C RPL23A 0.318 0.0203 0.7465 1.39 × 10^−10
BraA03g047490.3C RPL15A 0.3514 0.0099 0.3096 0.0241
BraA04g006490.3C RPL24B 0.3209 0.0191 0.4076 0.0025
BraA04g014040.3C RPS10B 0.3328 0.0149 0.4193 0.0018
BraA05g028570.3C RPL30B 0.366 0.007 0.4511 0.0007
BraA06g029850.3C RPL6 0.4174 0.0019 0.4623 0.0005
BraA06g032890.3C 0.3173 0.0206 0.4261 0.0015
BraA07g012190.3C RPL17B 0.3825 0.0047 0.3783 0.0052
BraA07g018220.3C RPL10AB 0.4172 0.0019 0.5375 3.32 × 10^−5
BraA07g022360.3C RPS26B 0.3703 0.0063 0.4319 0.0012
BraA07g031310.3C RPL17B 0.3695 0.0065 0.4108 0.0022
BraA08g004460.3C RPL18 0.3652 0.0072 0.5671 9.55 × 10^−6
BraA09g019250.3C ARP1 0.2793 0.0428 0.357 0.0087
BraA09g022110.3C RPL32A 0.3953 0.0034 0.4913 0.0002
[107]Open in a new tab
In the terpenoid backbone biosynthesis metabolic pathway, eight genes
had a significant negative correlation between gene expression levels
in the parents and hybrid PGW with correlation coefficients ranging
from −0.42 to −0.34 ([108]Table 8). Among them, the parental expression
level of BraA01g044250.3C (BrIPP2) had the highest correlation with
hybrid PGW. The parental expression level of these genes was also
significantly and negatively related to the MPH of PGW in hybrids, with
a correlation coefficient ranging from −0.57 to −0.36. Among them, the
parental expression level of BraA08g025620.3C (BrICMEL1) showed the
highest correlation with the MPH of PGW.
Table 8.
Correlation between hybrid PGW and parental gene expression levels in
terpenoid main chain biosynthesis.
GeneID Symbol PGW MPH of PGW
cor Q Value cor Q Value
BraA01g044250.3C IPP2 −0.4225 0.0016 −0.3671 0.0069
BraA02g023510.3C HMG1 −0.3538 0.0094 −0.3694 0.0065
BraA02g028120.3C HMGS −0.3386 0.0131 −0.5465 2.30× 10^−5
BraA03g011760.3C −0.353 0.0095 −0.3878 0.0041
BraA06g027360.3C FLCY −0.3564 0.0088 −0.5517 1.85 × 10^−5
BraA07g002120.3C −0.3555 0.009 −0.4595 0.0005
BraA08g025620.3C ICMEL1 −0.391 0.0038 −0.5731 7.30 × 10^−6
BraA09g014810.3C ISPF −0.3647 0.0073 −0.3582 0.0084
[109]Open in a new tab
4. Discussion
In Chinese cabbage, the selection of outstanding hybrids is also
concentrated in the continual process of trying, which not only wastes
a lot of manpower and material resources and causes economic waste but
also severely hinders the use of heterosis. The key to using the
heterosis is evaluating the parents of outstanding hybrids, but the
evaluation process is time-consuming and labor-intensive and becomes
the bottleneck of hybrid breeding. Despite this, it is not always
possible to obtain strong heterosis hybrids by crossbreeding using
excellent parents. To improve the breeding efficiency, previous efforts
have attempted to develop a variety of methods for predicting
heterosis, including combining the ability method, physiological and
biochemical method and molecular marker method.
With the rapid development of various kinds of omics, the application
of transcriptome data has emerged as a new approach for predicting
heterosis. Frisch et al. discovered that the transcriptome-based
distance was significantly correlated with the phenotype and heterosis
in maize hybrids [[110]23]. In maize, the proportion of genes with an
additive expression pattern in all genes was significantly positively
correlated with the phenotype and heterosis of hybrids [[111]40]. These
results indicated that transcriptome data can be used to predict
heterosis. In this study, the association between the expression of the
parental genes and the performance of hybrids was examined using
transcriptome data. The results showed that the number of DEGs between
parents was related to the field observation of PGW, LOL, LHH, LHW, HW,
LNH and PH, and the number of up-regulated genes was related to these
traits. In addition, Euclidean and binary distances of parental gene
expression levels were significantly correlated with the PGW, LOL, LHH,
LHW, HW and PH of hybrids. These results show that transcriptome data
can preliminarily predict phenotype in Chinese cabbage hybrids.
Therefore, the prediction of heterosis based on transcriptome has a
significant potential to increase the effectiveness in a hybrid
breeding program in some crops.
In hybrids, some parental gene expression levels were related to the
performance of hybrids and could be used as predictors of hybrid
performance. Thiemann et al. observed a significant correlation between
gene expression levels in parents and traits and heterosis in hybrid.
In Arabidopsis thaliana, a decrease in the abundance of At3g11220
transcripts in the parents was significantly correlated with an
increase in biomass heterosis in the corresponding region [[112]24]. In
maize, compared to other genes, the transcriptional abundance of
AGAMOUS-like protein in parents showed the most significant correlation
with hybrid traits [[113]41]. The purpose of this study is to identify
genes related to the PGW of hybrids by calculating the correlation
among mid-parent expression level, high-parent expression level,
low-parent expression level and PGW in hybrids. The results revealed
that the expression level of multiple genes in the ribosome metabolism
pathway was positively correlated with observation and the MPH of PGW,
with the BrRPL23A gene showing the highest correlation with the MPH of
PGW (r = 0.75), while the expression level of multiple genes in the
terpenoid backbone biosynthesis metabolic pathway was negatively
correlated with observation and MPH of PGW. Therefore, the parental
expression levels of some genes were related to hybrid phenotypes, can
preliminarily predict the heterosis and could provide reference for
parents’ selection.
In the ribosome metabolism pathway, the expression level of BrRPL23A in
parents had the highest correlation with heterosis of PGW. In
Arabidopsis, RPL23A is a part of a generally conserved protein, located
in the cytoplasm, and directly binds to large molecular subunit (LSU)
RNA, which is necessary for ribosome biosynthesis [[114]42]. AtRPL23Aa
gene knockout can lead to plant growth retardation, leaf irregularity,
leaf abscission, loss of root morphology and apical dominance, and the
function of RPL23A is crucial for plant survival in Arabidopsis
thaliana [[115]43]. In conclusion, the parental expression level
BrRPL23 may related to the heterosis in Chinese cabbage and could be
used to simply predict PGW of hybrids.
5. Conclusions
We concluded that parental DEGs number and transcriptome-based distance
were related to hybrid phenotypes and could preliminarily predict
hybrid phenotypes and select parents. In the ribosome metabolism
pathway, the parental expression level of BrRPL23 could be used to
simply predict PGW of hybrids.
Author Contributions
Conceptualization, L.Z. and R.L.; methodology, L.Z. and R.L.; data
curation, R.L. and M.T.; validation, R.L. and M.T.; writing—original
draft preparation, L.Z., R.L., M.T. and Q.H.; writing—review and
editing, L.Z., R.L., Q.H. and M.T.; funding acquisition, L.Z. All
authors have read and agreed to the published version of the
manuscript.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The RNA-seq data have been deposited with the NCBI with the dataset
identifier PRJNA876066.
Conflicts of Interest
The authors declare no conflict of interest.
Funding Statement
This work was funded by the Key Research and Development Program of
Yangling Seed Innovative Center (Ylzy-sc-04) and the National Key
Research and Development Program of China (2017YFD0101802).
Footnotes
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References