Abstract
Background
Kernel dehydration is an important factor for the mechanized harvest in
maize. Kernel moisture content (KMC) and kernel dehydration rate (KDR)
are important indicators for kernel dehydration. Although quantitative
trait loci and genes related to KMC have been identified, where most of
them only focus on the KMC at harvest, these are still far from
sufficient to explain all genetic variations, and the relevant
regulatory mechanisms are still unclear. In this study, we tried to
reveal the key proteins and metabolites related to kernel dehydration
in proteome and metabolome levels. Moreover, we preliminarily explored
the relevant metabolic pathways that affect kernel dehydration combined
proteome and metabolome. These results could accelerate the development
of further mechanized maize technologies.
Results
In this study, three maize inbred lines (KB182, KB207, and KB020) with
different KMC and KDR were subjected to proteomic analysis 35, 42, and
49 days after pollination (DAP). In total, 8,358 proteins were
quantified, and 2,779 of them were differentially expressed proteins in
different inbred lines or at different stages. By comparative analysis,
K-means cluster, and weighted gene co-expression network analysis based
on the proteome data, some important proteins were identified, which
are involved in carbohydrate metabolism, stress and defense response,
lipid metabolism, and seed development. Through metabolomics analysis
of KB182 and KB020 kernels at 42 DAP, 18 significantly different
metabolites, including glucose, fructose, proline, and glycerol, were
identified.
Conclusions
In sum, we inferred that kernel dehydration could be regulated through
carbohydrate metabolism, antioxidant systems, and late embryogenesis
abundant protein and heat shock protein expression, all of which were
considered as important regulatory factors during kernel dehydration
process. These results shed light on kernel dehydration and provide new
insights into developing cultivars with low moisture content.
Supplementary Information
The online version contains supplementary material available at
10.1186/s12870-023-04692-z.
Keywords: Kernel dehydration rate (KDR), Kernel moisture content (KMC),
Proteomics, Metabolomics, Maize
Background
Maize (Zea mays L.) is one of the main crops cultivated globally, and
whole-process mechanization is becoming more common in maize
production. However, most commercial varieties in China are not well
suited for mechanical harvest with the kernel, which is the major
bottleneck for maize production [[45]1]. One important limiting factor
is the high kernel moisture content (KMC) at harvest, which results in
higher kernel breakage and an increased impurity rate. These
limitations greatly increase the cost of harvest, reduce the economic
benefit, and affect seed quality [[46]2, [47]3]. According to previous
research, 15%-25% KMC is the optimal level for mechanized harvest;
however, in the main production area of China, the Huang-Huai-Hai
region, the KMC ranged from 21.5%-33.1% with an average of 27.8% for
summer maize [[48]4]. Furthermore, decreased KMC in medium-maturing
maize hybrids is considered to balance the KMC and yield for mechanized
grain harvest, as the lower grain moisture mainly resulted from
the > 30% dry matter in stover translocated to grain [[49]5].
Therefore, adjusting the germplasm to produce low-KMC maize for
improved mechanized harvest has become a research hotpot for breeders.
In recent decades, considerable scientific advances have been aided by
understanding the regulatory mechanisms of kernel development related
to kernel size, metabolism, and nutrient content including protein,
starch, oil, and flavonoids [[50]6–[51]8]. Physiological analysis of
kernel development has revealed that the process of kernel dehydration
is divided into two stages: developmental water loss and physical
dehydration [[52]9]. The first stage is a grain filling stage, which is
considered as physiological maturation due to the accumulation of dry
matter. The second stage is the post-physiological maturity stage,
during which the kernel undergoes rapid dehydration and gradually
transitions into dormancy [[53]10]. It was found that KMC and kernel
dehydration rate (KDR) are related to growth period, and maize with
shorter growth periods always have a faster dehydration rate [[54]11].
Additionally, KMC and KDR have been shown to be affected by extraneous
factors, including bract length, bract layer number, cob size, grain
number per ear, and climate [[55]2, [56]12].
In recent years, research investigating the genetic mechanisms
influencing KMC and KDR has increased. KMC and KDR can be affected by
the characterization of germplasm, cultivation method, and environment
[[57]13]. Nonetheless, it shows high broad-sense heritability
(0.66–0.85), and multiple quantitative trait loci (QTL) that control
KMC were identified using genome-wide linkage and association analysis
[[58]14–[59]19]. For instance, 31 KMC QTL and 17 KDR QTL were
identified in three environments using recombinant inbred lines
constructed from the inbred lines 844 and 807 [[60]20]. Meanwhile,
seven single-nucleotide polymorphisms (SNPs) associated with KDR have
been identified using a genome-wide association study (GWAS) of 309
inbred maize lines. Of these, one candidate gene (Zm00001d047468,
Zmapt1) has been found to be expressed differently in maize inbred
lines with different KDR [[61]21]. Recently, 71 QTL that influence KMC
were identified through a GWAS using 513 diverse inbred maize lines.
GRMZM5G805627 (ZmGAR2) and GRMZM2G137211 (ZmCRY1-9) were confirmed as
candidate genes for controlling KMC through a combination of genetic
population analysis, transcription profiling, and gene editing
[[62]22]. Owing to the complex characterization of KMC and KDR,
extensive research is required to understand the whole genetic basis.
Multiple omics analyses, including genomics, transcriptions,
proteomics, and metabolomics, have been used to explore the regulatory
mechanisms of kernel dehydration in crops. For instance, 11 proteins
related to dehydration tolerance have been detected at seed maturity in
maize using two-dimensional electrophoresis and mass spectrometry
[[63]23]. Yu et al. identified four oleosins and 76 stress/defense
proteins in maize during the maturation stage (40–50 days after
pollination, DAP), which may protect seeds from damage [[64]24]. Chen
et al. elucidated that the expression of late embryogenesis abundant
(LEA) protein, heat shock protein (HSP), and serpins was increased
during the dehydration stage using iTRAQ-based proteomics [[65]25].
Although previous research has demonstrated significant progress
regarding dehydration mechanisms, further work is required to clarify
the regulatory mechanisms at the proteome and metabolome levels.
According to a recent study using time-resolved multiomics analysis to
reveal the genetics of KMC and KDR in maize, 42 DAP was considered as a
key time point for KMC transformation in late kernel development
[[66]26]. Data from production practice suggested that 35–49 DAP is
when the dynamic processes from physiological maturity to dehydration
occur [[67]27]. Although association and linkage analyses are used to
identify QTL and genes related to kernel dehydration, they only focus
on the kernel moisture at harvest. Moreover, there are more ways to
understand the related regulation mechanism, especially the dehydration
rate. In the present study, we detected the proteome differences in
three maize inbred lines (KB182(A), KB207(K), and KB020(B)) with
differing KMC and KDR at 35, 42, and 49 DAP. Our objective was to
identify protein datasets influencing KMC and KDR during the key
dehydration period. Additionally, by integrating metabolome analysis,
the aims were to 1) establish the relationship between metabolism and
KMC and KDR, and 2) construct a preliminary regulation network for KMC
and KDR to provide a resource for further understanding the molecular
mechanisms to reduce maize KMC at harvest.
Results
Variation of kernel moisture content and kernel dehydration rate
To reduce the effect of the environment on KMC and KDR, the sowing time
was adjusted to ensure that the three inbred lines were pollinated on
the same day. Then, the ears of the three maize inbred lines pollinated
on the same day were used for KMC determination. All three inbred lines
showed a trend for fast dehydration from 7 to 49 DAP, shifted to
relative stability through 49 to 63 DAP, and then had a small fast
decline in hydration from 63 to 70 DAP (Fig. [68]1A and Table S[69]1).
At the earlier stages, KMC was lowest in KB182 and highest in KB020.
The variation trends of KB182 and KB020 were similar from 35 to 49 DAP,
but both differed compared to the trend of KB207. The KMC of KB207
dropped sharply from 35 to 42 DAP; thereafter, it did not change
markedly and ranged between 42 and 49 DAP. In addition, we evaluated
KDR using area under the dry down curve (AUDDC) value as previously
described [[70]28]. KB182 had the fastest KDR, whereas KB020 had the
slowest KDR among the three inbred lines in the whole-kernel
development process. For KB207, the KDR is fast at 35 to 42 DAP and
slow at 42 to 49 DAP, which resulted in its KMC being closer to that of
KB020 at 35 and 49 DAP but differed significantly at 42 DAP when it was
more similar to that of KB182 (Fig. [71]1B and Table S[72]1).
Therefore, the three inbred lines with different phenotype were
suitable to study the mechanisms behind KMC and KDR.
Fig. 1.
[73]Fig. 1
[74]Open in a new tab
Variant of moisture content of different inbred lines in different
periods. A Changes in moisture content of three inbred lines at
7–70 days after pollination. B Changes in AUDDC of three inbred lines
at AUDDC1-AUDDC9
Proteome dynamics during the dehydration process
To explore the critical proteins in the key dehydration processes of
maize seeds, we detected and quantified proteins in the kernels from
KB182, KB020, and KB207 at 35, 42, and 49 DAP. Proteins were quantified
based on data-independent acquisition (DIA) proteomics. In total,
28,372 peptides were detected, and most identified peptides were 9–19
amino acids in length (Fig. [75]2A). Of the 9,241 proteins identified,
8,358 of them were qualified, and among them, 96 proteins were sourced
from Swiss-Prot and 8,262 from TrEMBL (Table S[76]2). The number of
identified proteins in each sample ranged from 6,984 to 7,704, and the
number of identified proteins decreased along with the dehydration
process (Fig. [77]2B). According to the principal component analysis
(PCA), the three biological replicates of each sample were strongly
correlated (Fig. S[78]1). The protein abundances were validated based
on parallel reaction monitoring (PRM) by selecting seven random
proteins: catalase isozyme 3 ([79]P18123), late embryogenesis abundant
protein Lea14-A (B6UH99), granule-bound starch synthase 1b
(A0A1D6HXP5), pathogenesis-related protein 10 (A0A1D6JZU3), HSP 26
([80]Q41815), L-ascorbate peroxidase (B4FWL1), and peroxidase (C0PKS1).
The results of PRM were consistent with DIA data (Fig. S[81]2). These
results show that protein abundance qualification by DIA was highly
effective and feasible.
Fig. 2.
[82]Fig. 2
[83]Open in a new tab
Basic information of all peptides and the number of identified proteins
in all samples. A Distribution of peptide length in samples. B
Distribution of proteins in samples
Identification and annotation of DEPs in different stage or different samples
To explore the important proteins for dehydration during different
growth periods and in different inbred lines, we compared different
inbred lines at the same time and the same inbred line at different
periods using the standard with log[2](fold change) > 1 or < − 1 in
expression and P value < 0.05. Finally, we obtained 2,779 different
expressed proteins (DEPs) after deleting the repetitive DEPs (including
all those shown in Tables S[84]3 and S[85]6). First, we compared the
samples from the same inbred lines at different stages, which included
nine comparison pairs: A35 vs A42, A42 vs A49, A35 vs A49, B35 vs B42,
B42 vs B49, B35 vs B49, K35 vs K42, K42 vs K49, and K35 vs K49, where
we defined KB182 as A, KB182 as B, and KB207 as K (Fig. [86]3A-C and
Table S[87]3). Then, 131 common DEPs were detected between the three
inbred lines (Fig. [88]3D), which may play an identical role in
regulating dehydration, and they were annotated in response to stress
(including that from water, external stimulus, chemicals, desiccation,
and oxidative stress) and growth (Table S[89]4). Furthermore, 12 common
proteins were detected in all comparative pairs whose functions include
response to stress, carbohydrate metabolism, gene expression
regulation, and material transport (Table S[90]5).
Fig. 3.
[91]Fig. 3
[92]Open in a new tab
Comparison of differentially expressed proteins (DEPs) in each inbred
line at different periods. A Number of DEPs of KB182 in three periods.
B Number of DEPs of KB020 in three periods. C Number of DEPs of KB207
in three periods. D Distribution of DEPs in three inbred lines
Second, another nine pairs were obtained by comparing the samples from
different inbred lines at the same stage of development: A35 vs B35,
A35 vs K35, B35 vs K35, A42 vs B42, A42 vs K42, B42 vs K42, A49 vs B49,
A49 vs K49, and B49 vs K49 (Fig. [93]4A-D and Table S[94]6). The number
of DEPs identified between KB182 and KB020 at all three stages was
consistently higher than that identified between KB182 vs KB207 and
KB207 vs KB020, which is consistent with the trends of KMC and KDR
(Fig. [95]1). Therefore, we focused on KB182 and KB020 to further
explore the key proteins and pathways related to dehydration.
Comparative analysis showed that there were 620, 746, and 690 DEPs
between KB182 and KB020 at 35 DAP, 42 DAP, and 49 DAP, respectively.
There are 235 common DEPs, which may be closely related to dehydration
at all time points (Table S[96]7). At the same time, we performed Gene
Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)
analyses on the DEPs at the three time points, and they were enriched
at all time points, including response to inorganic substance, response
to oxygen-containing compound, catalytic activity, metabolic pathways,
biosynthesis of secondary metabolites, and carbohydrate metabolism
(fructose and mannose metabolism, amino sugar and nucleotide sugar
metabolism, and glycolysis/gluconeogenesis) (Table S[97]8).
Fig. 4.
[98]Fig. 4
[99]Open in a new tab
Comparison of differentially expressed proteins (DEPs) in different
inbred lines in the same period. A Number of DEPs of three inbred lines
at 35 days after pollination (DAP). B Number of DEPs of three inbred
lines at 42 DAP. C Number of DEPs of three inbred lines at 49 DAP. D
Distribution of DEPs in the three periods. E Gene Ontology enrichment
results of key proteins after comparison. F Kyoto Encyclopedia of Genes
and Genomes enrichment results of key proteins after comparison
According to our previous division of dehydration for three inbred
lines, to better understand the regulation of KMC, 246 common DEPs
between the comparisons A35 vs B35 and A35 vs K35 were used for deeper
analysis due to the KMC of KB020 and KB207 being closer but differing
significantly from the KMC of KB182. Similarly, 256 common DEPs between
the comparisons A42 vs B42 and B42 vs K42 and 183 common DEPs between
the comparisons A49 vs B49 and A49 vs K49 are also considered key
proteins. These DEPs were mainly enriched in response to water,
oxidoreductase activity, fructose and mannose metabolism, pentose and
glucuronate interconversions, biosynthesis of secondary metabolites,
and glycine, serine, and threonine metabolism (Fig. [100]4E, F and
Table S[101]9).
Relationship of protein abundance with KMC and KDR
To determine the relationship between protein abundance and KMC
phenotype, we compared the curves of observed dehydration and protein
abundance using K-means clustering with the quantified protein
(Fig. [102]5A-C). Proteins were then divided into 10 clusters for each
of the three inbred lines. For KB182 and KB020, the parallel
dehydration curves showed fast dehydration from 35 to 49 DAP;
therefore, we focused on the clusters 1, 4, and 9 of KB182 and clusters
2, 4, and 8 of KB020 because they showed a high expression abundance at
these three time points (Fig. [103]5A, B). In total, 1,787 DEPs were
found between KB182 and KB020 that were enriched in response to stress
(including oxidative stress, and response to stimulus and water), small
molecule metabolic processes, antioxidant activity, fatty acid
metabolism, carbohydrate metabolism (glycolysis/gluconeogenesis,
fructose and mannose metabolism, pentose phosphate pathway, and starch
and sucrose metabolism), carbon metabolism, and glutathione metabolism
(Fig. [104]5D-F and Table S[105]10). For KB207, whose dehydration was
fast from 35 to 42 DAP but slow from 42 to 49 DAP, a similar trend was
observed in cluster 4 with 69 DEPs, which were associated with
oxidation reduction (P = 1.10E-08 and FDR = 4.90E-07) in the GO
analysis.
Fig. 5.
[106]Fig. 5
[107]Open in a new tab
Expression patterns of three inbred lines at three time points. A
Protein expression pattern of KB182. B Protein expression pattern of
KB020. C Protein expression pattern of KB207. D Venn diagram of
high-expression proteins in KB182 and KB020. E Gene Ontology enrichment
results of 1,787 high-expression proteins. F Kyoto Encyclopedia of
Genes and Genomes enrichment results of 1,787 high-expression proteins
We systematically investigated the relationship between protein
abundance and dynamic KMC using the correlation weighted values between
different protein expressions with weighted gene co-expression network
analysis (WGCNA) (Fig. [108]6A). All expressed proteins were divided
into 42 modules (Table S[109]2), in which six modules were
significantly correlated with KMC and KDR using the thresholds of
R > 0.6 and P < 0.05, including light cyan (65), cyan (259), orange
(115), dark olive green (88), green (2469), and gray (133)
(Fig. [110]6B). These proteins were found to participate in response to
abiotic stimuli, small molecule metabolic processes, amide
biosynthesis, carbohydrate metabolism (the pentose phosphate pathway,
glycolysis/gluconeogenesis, citrate cycle, and fructose and mannose
metabolism), glutathione metabolism, and biosynthesis of secondary
metabolites, among others (Fig. [111]6C, D and Table S[112]11). An
interaction network was constructed using proteins of the top 5%
connectivity as hub proteins in important modules (Fig. [113]7 and
Table S[114]12). This network showed that the unique interaction
between the cyan and green modules was linked by C4J8S0 and C0PL14.
These hub proteins were involved in antioxidant systems and
carbohydrate, energy, and lipid metabolism. In addition, these hub
proteins were mainly enriched in response to water, organonitrogen
compound metabolic processes, cellular amide metabolic processes, and
ribosomes (Table S[115]13).
Fig. 6.
[116]Fig. 6
[117]Open in a new tab
Weighted gene co-expression network analysis identified a
dehydration-related module. A Protein expression module. B
Relationships between the modules and dehydration. C Gene Ontology
enrichment results of important modules. D Kyoto Encyclopedia of Genes
and Genomes enrichment results of important modules
Fig. 7.
[118]Fig. 7
[119]Open in a new tab
Protein–protein interaction network of hub proteins (Top 5%)
Variation in metabolic components in KB182 and KB020
Based on phenotypic results, we found a significant difference in KMC
between KB182 and KB020 when it reached its maximum at 42DAP during our
proteomic testing of the three stages (Fig. [120]1). Moreover, the
number of DEPs in A42 vs B42 was the highest among different inbred
lines compared in the three stages. Therefore, we chose KB182 and KB020
with 42DAP for metabolomic analysis.A total of 151 metabolites were
detected, and the metabolic components in these two inbred lines showed
considerable sample separation (Fig. [121]8A). Of these, 18 metabolites
had significantly different abundance and were mainly associated with
carbohydrate, amino acid, and energy metabolism (Table S[122]14).
Fig. 8.
[123]Fig. 8
[124]Open in a new tab
Metabolome analysis between KB182 and KB020. A Partial least-square
discriminant analysis (PLS-DA) score plot of samples in different
lines. B Top 15 most closely associated metabolites and proteins
By integrating the metabolome and proteome analyses using two-way
orthogonal partial least square with discriminant analysis (O2PLS), the
joint parts (R2 proteomicCORR and R2 metabolomeCORR) indicated that
more than 80% of the metabolomic and proteomic variation was explained.
The most closely associated metabolites and proteins included ubiquitin
carrier protein, aldolase-type TIM barrel family protein, nonspecific
lipid-transfer protein, and germin-like protein. Metabolites with
strong correlation with proteins included sucrose, isocitric acid,
phosphate, and malic acid (Fig. [125]8B).
Discussion
With the increasing population and decreasing labor, agricultural
production has become more intensive and mechanized, thus requiring
maize varieties suitable for kernel-mechanized harvesting, which can
save both time and labor. KMC and KDR are important factors affecting
mechanized maize harvesting [[126]29, [127]30]. According to precious
study, KB182, KB207 and KB020 showed significant differences in KMC and
KDR, which were bred from the Shaan B group. The KDR of KB182 is fast
throughout the whole dehydration process, and the KMC is always low. In
contrast the KDR of KB020 is slow throughout the dehydration process,
and the KMC is always high. However, KB207 has a rapid decrease in KDR
at 35–42 DAP and then tends to stop. The differences between KMC and
KDR make them very suitable for studying kernel dehydration.
Additionally, the previous study only analyzes KMC at the harvest stage
and focuses on gene variation [[128]15, [129]16]. Proteins and
metabolites have not been explored yet, and the related metabolic
pathways are still unclear. Therefore, we coordinated pollination on
the same day to minimize environmental noise and compared the
differences in protein and metabolism abundance to explore key proteins
and metabolites and understand the regulatory mechanisms of KMC and
KDR.
In this study, 2,779 DEPs were identified that may be involved in
regulating kernel dehydration, and 12 of them were identified at all
stages of different inbred lines, which may play a role throughout all
stages of dehydration. At the same time, we identified the metabolomics
of KB182 and KB020 at 42DAP to understand the relevant mechanisms
affecting kernel dehydration from the perspective of metabolites. We
detected a total of 18 differential metabolites, of which 7 were
carbohydrates, and 6 were amino acids, indicating that carbohydrates
and amino acids may be the main reasons for the differences in KMC and
KDR between KB182 and KB020.Futhermore, we compared DEPs with the QTL
and SNP mapped by previous research [[130]31–[131]36]. Finally, 441
DEPs were identified as candidate proteins. (Table S[132]15). These
results provide the possibility for subsequent understanding regulation
mechanism of kernel dehydration.
As products of plant life activities, reactive oxygen species (ROS) can
participate in signal transduction, growth, and development but are
accumulated excessively in seeds during the rapid dehydration stage,
which causes irreversible cell damage [[133]37]. Therefore, the
effective operation of antioxidant systems is crucial for the seed to
resist damage during dehydration [[134]38, [135]39]. We observed the
enrichment of eight main antioxidant system enzymes, including five
peroxidases (B4F7T9, C0PKS1, B4FBH0, C0HEE6, and C0P813), two catalases
([136]P18123 and [137]P12365), and one superoxide dismutase (B1PEY4),
in our inbred maize lines. The same phenomenon was observed in a recent
study in which peroxidase and superoxide dismutase were phosphorylated,
and their activity was decreased after severe dehydration stress;
however, this did not seem to be sufficient to eliminate ROS and lead
to increased H[2]O[2] levels [[138]40]. The ascorbate–glutathione cycle
is another important regulator of H[2]O[2] scavenging. We found three
key proteins located in the green and cyan modules, including
dehydroascorbate reductase (C0P9V2), which degrades docosahexaenoic
acid to ascorbic acid, and glutathione reductase (B4FWU6 and
A0A1D6JPH3), a catalyst for the regeneration of glutathione [[139]41].
These proteins might affect kernel dehydration via
ascorbate–glutathione cycle regulation, which has not been reported to
date.
Previous studies considered that carbohydrates participate in the
protection of seeds in two ways: water substitution and vitrification
of water phase [[140]42, [141]43]. Trehalose was first found to play an
important role in plant dehydration, but subsequent studies found that
sucrose plays a similar role [[142]44]. Raffinose-family
oligosaccharides accumulate during seed maturation and play important
roles in seed vigor [[143]45]. In addition, the raffinose:sucrose ratio
can influence membrane stability and protect high-moisture seeds from
dehydration in maize [[144]46]. The present study indicated that
sucrose synthase (A0A1D6P836, C0P6F8) and sucrose phosphate synthase
(A0A1D6N358) were more abundant in high-moisture inbred lines and
during the early stages of growth. Previous studies have shown that the
expression of glucose and fructose first increased, then decreased, and
finally disappeared during seed development [[145]47]. This indicates
that glucose and fructose do not play the same role as sucrose in the
process of dehydration, and they may be used as raw materials to
synthesize sucrose [[146]48]. Similarly, we observed an accumulation of
fructose, glucose, and related proteins, and a series of enzymes
related to starch metabolism (B4FYM6, [147]Q5NKP6, A0A1D6K8T3,
[148]Q9SYS1, and B5AMJ8) in the inbred lines. These results indicated
that carbohydrates, especially raffinose and sucrose, can regulate
kernel dehydration (Table S[149]16).
Previous studies have divided the protective proteins related to
dehydration into two categories: LEA proteins and HSPs. These proteins
are activated by abscisic acid (ABA), accumulate at the later stage of
seed embryo development, are abundant in dry seeds, and play defensive
and protective roles in seed dehydration [[150]49–[151]51]. In the
present study, five LEA proteins, encoded by Zm00001d009382,
Zm00001d034002, Zm00001d038870, Zm00001d043709, and Zm00001d017021,
showed higher expression later in kernel development, which is
consistent with the results of others [[152]25, [153]52]. In addition,
their abundance was highest in KB182 and lowest in KB020 of the same
stage, indicating that a higher LEA protein abundance might cause
faster dehydration. One HSP (Zm00001d015777) was identified as a DEP in
this study, which was related to the kernel dehydration trend and LEA
protein expression. These results indicate that LEA protein and HSPs
play a crucial role in maize kernel dehydration and may regulate kernel
dehydration through a similar mechanism in maize; however, further
research is required to validate this.
According to our integrative analysis of the proteome and metabolome,
we propose a model of how LEA proteins, HSPs, antioxidant systems, and
carbohydrate metabolism (raffinose and sucrose) are involved in
regulating kernel dehydration (Fig. [154]9). In particular, when the
kernel enters the stage of rapid dehydration, ABA synthesis is
activated after the dehydration pressure is felt inside the kernel. ABA
induces the expression of LEA proteins and HSPs in response to kernel
dehydration [[155]53]. At the same time, the dynamic balance of ROS
production and scavenging is disrupted, and the antioxidant system
begins to work effectively to remove ROS. However, in the rapid
dehydration stage, this defense method is gradually weakened, and
sucrose metabolism replaces water molecules to compensate for the
deficiency of the antioxidant system and maintain cell membrane
stability [[156]54]. A limitation of our study was that we were unable
to verify the function of the nominated protein at the molecular level.
However, the results we obtained by minimizing the environmental noise
are helpful to understand the molecular mechanism of kernel dehydration
and provide a theoretical basis for mechanized-harvest breeding. In
future, more detailed work is necessary to understand the genetic basis
through traditional map-based cloning and validate some core genes
through gene editing for breeding improved maize.
Fig. 9.
[157]Fig. 9
[158]Open in a new tab
Role of different types of protein during maize kernel dehydration. AA
(ascorbic acid); ROS (reactive oxygen species); APX (ascorbate
peroxidase); CAT (catalase); DHA (dehydroascorbate); DHAR
(dehydroascorbate reductase); GR (glutathione reductase); GSH
(glutathione); GSSG (glutathione); H[2]O[2] (hydrogen peroxide); MDA
(monodehydroascorbate); MDAR (monodehydroascorbate reductase); NADPH
(nicotinamide dinucleotide phosphate); SOD (superoxide dismutase); AMY
(α-amylase); α-GLU (glucosidase); HK (hexokinase); SPS
(sucrose-phosphate synthase); SP (sucrose phosphorylase); SPP
(sucrose-6-phosphatase); PYG (glucan phosphorylase); PGM
(phosphoglucomutase); UGP2 (UTP–glucose-1-phosphate
uridylyltransferase); sHSP (small HSP); LEA (late embryogenesis
abundant) protein
Methods
Field experiment and phenotyping
Inbred lines KB182, KB020, and KB207 were bred from the Shaan A and
Shaan B groups by the maize biology and genetic breeding group at
Northwest A&F University, Shaanxi, China. To ensure that all ears were
pollinated on the same day and minimize environmental noise, the three
inbred lines KB182, KB207, and KB020 were sown on May 11th, 19th, and
17th in 2019, respectively. All field experiments were conducted with
three replications in the Yangling maize base of Northwest A&F
University. After unified pollination on July 25th, 100 kernels were
collected from each ear at 10 successive stages (7, 14, 21, 28, 35, 42,
49, 56, 63, and 70 DAP). After sampling, kernel fresh weight (W1) was
measured with a 0.001 g digital scale. Then, the samples were heated in
an oven at 105℃ for 30 min and finally dried at 70℃ to constant weight
(W2). The formulae for KMC and area under the dry down curve used were
as previously described [[159]26, [160]28] and are as follows:
[MATH: KMC(%)=[(W1-W2)/W1]×100%, :MATH]
Where W1 is the kernel fresh weight, and W2 is the final weight after
drying. According to KMC, KDR was calculated using the AUDDC method
using the following formula:
[MATH: AUDDC=∑i
1KMCi+KMCi+1/2ti+1-ti
, :MATH]
Where KMC is the moisture content of the kernel,
[MATH: i :MATH]
is the
[MATH: ith :MATH]
measured time, and
[MATH: ti :MATH]
is the corresponding day after pollination (7, 14, 21, 28, 35, 42, 49,
56, or 70).
Nine AUDDC traits were established based on the phenotype at 10 time
points: AUDDC1 (7–14 DAP), AUDDC2 (14–21 DAP), AUDDC3 (21–28 DAP),
AUDDC4 (28–35 DAP), AUDDC5 (35–42 DAP), AUDDC6 (42–49 DAP), AUDDC7
(49–56 DAP), AUDDC8 (56–63 DAP), and AUDDC9 (63–70 DAP). The lower the
AUDDC, the faster the KDR.
Protein extraction and digestion
According to the variation in KMC, samples taken on 35, 42, and 49 DAP
from KB182, KB020, and KB207 were used for protein extraction and
analysis; these samples were named accordingly (A35, A42, A49, B35,
B42, B49, K35, K42, and K49), where A is KB182, B is KB020 and K is
KB207. Three biological replicates of the samples at each time point
were mixed as a pooled sample. Protein extraction, peptide preparation,
and quantification of each sample were conducted in triplicate using
mass spectrum (MS) analysis by PTM Biolab LLC based on DIA technology
as previously described [[161]55]. The sample was first ground with
liquid nitrogen, and then the powder was transferred to a 5-mL
centrifuge tube and sonicated for 3 min on ice using a high-intensity
ultrasonic processor (Scientz) in lysis buffer. An equal volume of
Tris-saturated phenol (pH 8.0) was added. Then, the mixture was further
vortexed for 5 min. After centrifugation (4 °C, 10 min, 5500 g), the
upper phenol phase was transferred to a new centrifuge tube. Proteins
were precipitated by adding at least four volumes of ammonium
sulfate-saturated methanol and incubated at − 20 °C for at least 6 h.
After centrifugation at 4 °C for 10 min, the supernatant was discarded.
The remaining precipitate was washed thrice with ice-cold methanol,
followed by ice-cold acetone. The protein was redissolved in 8 M urea.
The sample was slowly added to the final concentration of 20% (m/v)
trifluoroacetic acid to precipitate protein, then vortexed to mix and
incubated for 2 h at 4 °C. The precipitate was collected by
centrifugation at 4500 g for 5 min at 4 °C. The precipitated protein
was dissolved in 200 mM triethyl-ammonium bicarbonate buffer and
ultrasonically dispersed. Trypsin was added at 1:50 trypsin-to-protein
mass ratio for the first digestion overnight. The sample was reduced
with 5 mM dithiothreitol for 60 min at 37 °C and alkylated with 11 mM
iodoacetamide for 45 min at room temperature in darkness. Finally, the
peptides were desalted by the Strata X SPE column.
Spectral library building—LC–MS/MS analysis
The tryptic peptides were dissolved in solvent A (0.1% formic acid, 2%
acetonitrile), and directly loaded onto a homemade reversed-phase
analytical column. Peptides were separated with a gradient from 4 to
32% solvent B (0.1% formic acid in 90% acetonitrile) over 114 min, 32%
to 80% in 3 min, and holding at 80% for the last 3 min. The separated
peptides were analyzed in data-dependent acquisition (DDA) mode by Q
ExactiveTM HF-X (Thermo Fisher Scientific) with a nano-electrospray ion
source.
Data-independent acquisition
The iRT kit was added to all the samples according to the
manufacturer’s instructions. The LC gradient was kept consistent with
those in the spectral library building method. The separated peptides
were analyzed in Q ExactiveTM HF-X (Thermo Fisher Scientific) with a
nano-electrospray ion source. The data acquisition was performed in DIA
mode. Each cycle contains one full scan followed by 70 DIA MS/MS scans
with a predefined precursor m/z range. The HCD fragmentation was
performed at a normalized collision energy of 27%.
All DIA data were analyzed in Skyline (v 20.1.0). The DDA search
results were imported to Skyline to generate the spectral library, and
the retention times were aligned to iRT reference values. Relative
quantification of proteins was performed using the MSstats package. PCA
was performed to evaluate the repeatability of all samples using
quantified proteins. To confirm DIA and data-dependent analysis data,
24 proteins were randomly selected and quantified using PRM analysis
performed by PTM Biolab LLC. DEPs were identified according to the
standard with P value < 0.05 and log[2](fold change) > 1 or < − 1.
Non-targeted metabolic profiling using GC–MS
Based on the variation in KMC and proteomics analyses, KB182 and KB020
with different KMC and KDR values were used for metabolic analysis at
42 DAP. Metabolic analysis was determined using a gas
chromatograph-mass spectrometer (GC–MS; 7890A-5975C, Agilent
Technologies, Palo Alto, CA, USA), and each sample was analyzed with
two biological and three technical replicates. The samples pre-cooled
in liquid nitrogen were ground using a Mixer/mill (MM400; Retsch) with
a steel ball for 30 s at 30 HZ. Fifty milligrams of Platycerium
wallichii Hook. powder of each sample was extracted following the
procedures described in a previous study [[162]56–[163]58]. The extract
was centrifuged at 23,128 g for 10 min at 4 °C. The fixed volume of 200
μL of the polar phase was transferred into a pre-labeled 1.5-mL
microcentrifuge tube. Then, the samples were dried in a SpeedVac
concentrator without heating. The dried 200 μL aliquots from the lower
phase for primary metabolite profiling were derivatized with
N-methyl-N-(trimethylsilyl) trifluoroacetamide as described previously
[[164]59] and further analyzed using GC–MS (7890A-5975C, Agilent, USA).
One µL was taken from each sample and injected into GC–MS at 270 °C in
a split mode (50:1) with helium carrier gas (> 99.999% purity) flow set
to 1 mL/min and separated by a DB-35MS UI (30 m × 0.25 mm, 0.25 µm)
capillary column. The temperature was isothermal for 4 min at 90 °C,
followed by an 8 °C increase per minute ramp up to 205 °C, then held
constant for 2 min, and finally ramped up at a rate of 15 °C per minute
to 310 °C and held constant for 2 min. The transfer line temperature
was set to 300 °C, and the ion source temperature was set to 230 °C.
The mass range analyzed was from m/z 85 to 700. The Agilent MassHunter
Qualitative Analysis software version B.06.00 (Agilent Technologies,
Palo Alto, CA, USA) and Agilent MassHunter Quantitative Analysis
software version B.07.01 (Agilent Technologies, Palo Alto, CA, USA)
were both used for GC–MS data analyses. The NIST library and in-house
database established using authentic standards were used together for
metabolite identification. Supervised partial least-square (PLS)
discriminant analysis was performed to construct a high level of group
separation. To obtain an overview of the model, data were fit to
highlight discriminant metabolites. The variable importance in
projection > 1 and P value < 0.05 were selected to determine
significantly different metabolites between different comparison
groups.
Bioinformatics analysis
In order to further describe the DEPs, the TBtools (v1.0692) software
was used to generate Venn diagrams [[165]60]. The qualified proteins
were clustered using the K-means cluster function in R
([166]http://www.r-project.org/). The WGCNA was performed using the
“WGCNA” package (v3.6.2) in R to determine the core expression protein
modules [[167]61]. A co-expression network was constructed via Gephi
(v0.9.2) using the identified proteins related to KMC and KDR with the
thresholds of r > 0.6 and P < 0.05 [[168]62]. MetaboAnalyst
([169]http://www.metaboanalyst.ca) was used to enrich important
metabolite pathways [[170]63]. The O2PLS model
([171]https://www.omicshare.com/tools) was used to determine related
metabolites and proteins by integrating proteomic and metabolome data.
Finally, the core proteins were enriched through GO analysis using the
agriGO web server ([172]http://bioinfo.cau.edu.cn/agriGO/index.php) and
KEGG pathway enrichment analysis with the Kobas web server
([173]http://kobas.cbi.pku.edu.cn/) [[174]64, [175]65]. FDR < 0.05 was
used as the threshold to obtain significantly enriched GO terms and
pathways.
Supplementary Information
[176]12870_2023_4692_MOESM1_ESM.docx^ (219KB, docx)
Additional file 1: Figure S1.Two-dimensional scatter plot of principal
component analysis (PCA) distribution of all samples using quantified
proteins.
[177]12870_2023_4692_MOESM2_ESM.docx^ (665.5KB, docx)
Additional file 2: Figure S2.Parallel reaction monitoring validation of
several proteins identified by the DIA data.
[178]12870_2023_4692_MOESM3_ESM.xlsx^ (10.7KB, xlsx)
Additional file 3: Table S1. KMC and AUDDC phenotype data of three
inbred lines.
[179]12870_2023_4692_MOESM4_ESM.xlsx^ (2.9MB, xlsx)
Additional file 4: Table S2. DIA quantitative information.
[180]12870_2023_4692_MOESM5_ESM.xlsx^ (637.9KB, xlsx)
Additional file 5: Table S3. Information of differentially expressed
proteins in different periods of the same inbred line.
[181]12870_2023_4692_MOESM6_ESM.xlsx^ (40.9KB, xlsx)
Additional file 6: Table S4. The common proteins detected in three
inbred lines.
[182]12870_2023_4692_MOESM7_ESM.xlsx^ (16KB, xlsx)
Additional file 7: Table S5. The common proteins detected in different
periods at same inbred line.
[183]12870_2023_4692_MOESM8_ESM.xlsx^ (972.4KB, xlsx)
Additional file 8: Table S6. Information of differentially expressed
proteins of different inbred lines in the same period.
[184]12870_2023_4692_MOESM9_ESM.xlsx^ (82.1KB, xlsx)
Additional file 9: Table S7. The common proteins detected in KB182 VS
KB020 at three periods.
[185]12870_2023_4692_MOESM10_ESM.xlsx^ (13.6KB, xlsx)
Additional file 10: Table S8. GO and KEGG enrichment of key proteins in
all comparison group.
[186]12870_2023_4692_MOESM11_ESM.xlsx^ (17.9KB, xlsx)
Additional file 11: Table S9. GO and KEGG enrichment of KB182 VS KB020
at three periods.
[187]12870_2023_4692_MOESM12_ESM.xlsx^ (22.1KB, xlsx)
Additional file 12: Table S10. GO and KEGG enrichment of proteins with
the same expression pattern of KB182 and KB020.
[188]12870_2023_4692_MOESM13_ESM.xlsx^ (36.5KB, xlsx)
Additional file 13: Table S11. GO and KEGG enrichment in different
modules.
[189]12870_2023_4692_MOESM14_ESM.xlsx^ (18.4KB, xlsx)
Additional file 14: Table S12. TOP 5% of hub protein identified by
WGCNA.
[190]12870_2023_4692_MOESM15_ESM.xlsx^ (13.3KB, xlsx)
Additional file 15: Table S13. GO and KEGG enrichment of hub protein
identified by WGCNA.
[191]12870_2023_4692_MOESM16_ESM.xlsx^ (49KB, xlsx)
Additional file 16: Table S14. GC-MS metabolite identification results.
[192]12870_2023_4692_MOESM17_ESM.xlsx^ (24.9KB, xlsx)
Additional file 17: Table S15. Proteins significantly associated with
KMC and KDR turough compare with previous study.
[193]12870_2023_4692_MOESM18_ESM.xlsx^ (12KB, xlsx)
Additional file 18: Table S16. Sugars significantly associated with
dehydration.
Acknowledgements