Abstract

   Both genetic improvement and the application of N fertilizer increase
   the quality and yields of wheat. However, the molecular kinetics that
   underlies the differences between them are not well understood. In this
   study, we performed a non-targeted metabolomic analysis on wheat
   cultivars from different release years to comprehensively investigate
   the metabolic differences between cultivar and N treatments. The
   results revealed that the plant height and tiller number steadily
   decreased with increased ears numbers, whereas the grain number and
   weight increased with genetic improvement. Following the addition of N
   fertilizer, the panicle numbers and grain weights increased in an old
   cultivar, whereas the panicle number and grain number per panicle
   increased in a modern cultivar. For the 1950s to 2010s cultivar, the
   yield increases due to genetic improvements ranged from −1.9% to 96.7%,
   whereas that of N application ranged from 19.1% to 81.6%. Based on the
   untargeted metabolomics approach, the findings demonstrated that
   genetic improvements induced 1.4 to 7.4 times more metabolic
   alterations than N fertilizer supply. After the addition of N, 69.6%,
   29.4%, and 33.3% of the differential metabolites were upregulated in
   the 1950s, 1980s, and 2010s cultivars, respectively. The results of
   metabolic pathway analysis of the identified differential metabolites
   via genetic improvement indicated enrichment in 1-2 KEGG pathways,
   whereas the application of N fertilizer enriched 2–4 pathways. Our
   results provide new insights into the molecular mechanisms of wheat
   quality and grain yield developments.

   Keywords: yield, grain quality, metabolome, wheat release year,
   nitrogen nutrient

1. Introduction

   Wheat (Triticum aestivum L.) is one of the world’s most widely
   cultivated crops and plays a critical role in ensuring national food
   security [[36]1]. The arable land in China accounts for ~7% of the
   world’s total; of the crops produced on this land, wheat production
   accounts for 20.30% and feeds 22% of the global total [[37]2]. In
   limited cultivated farmlands, food security must be ensured through
   continuous increases in grain yields. Since 1949, China’s wheat
   cultivars have undergone 4–6 large-scale replacements [[38]3]. Prior to
   the 1980s, yield increases, stress resistance, and regional
   adaptability were the primary drivers of the replacements of wheat
   cultivars [[39]4]. However, with economic development, improvements in
   individual living standards, and changes in dietary structure, wheat
   production structures were adjusted to adapt to the market,
   particularly flour enterprises that placed higher requirements on wheat
   quality [[40]5]. Several studies have reported the evolution of wheat
   quality and yields during cultivar improvement [[41]6,[42]7]. Due to
   differences in the cultivar and ecological environments, the
   conclusions of these different studies are not entirely consistent.

   Historical sets of various cereal crops were extensively evaluated in
   earlier studies to improve the data regarding genetic advances in grain
   yield and the associated physiological traits [[43]8]. Two major
   breakthroughs in breeding contributed to the dramatic increase in
   average wheat yields over the last half-century. The first was the
   Green Revolution, which saw the rise of dwarf plant breeding, whereas
   the second was the distant hybridization between wheat and its wild
   relatives [[44]9,[45]10]. In addition to genetic improvements, N
   fertilizer plays a decisive role in agricultural production [[46]11].
   The proper application of N fertilizer increases the absorption areas
   and activities of crop root systems to promote the emergence of
   productive wheat tillers [[47]12,[48]13]. According to FAO data, ~55%
   of the increase in grain production in developing countries has been
   derived from chemical fertilizers. In the early 1970s, China began to
   apply large quantities of N fertilizers. With the rapid development of
   the fertilizer industry, China has become the largest consumer and
   producer of N fertilizer in the world [[49]14]. Nitrogen directly or
   indirectly affects many aspects of the metabolism, growth, and
   development of plants, and is the main element that impacts the quality
   and yields of wheat [[50]15]. Researchers have previously described the
   kinetics of N for improving crop quality and yield from the
   perspectives of agronomic traits, photosynthetic physiology, ecological
   effects, and nutrient absorption and transport [[51]16,[52]17].

   Metabolomics has become a powerful tool in the post-genomics era, which
   enables the exploration of different facets of biological and
   physiological changes caused by environmental or genetic perturbations
   [[53]18]. Xv et al. [[54]19] compared the predictive effects of various
   omics on crop yields, with the results showing that the metabolome
   produced the highest predictability followed by the transcriptome and
   genome. Metabolites have been used to predict plant phenotypes and
   quality [[55]20,[56]21]. For example, crop engineering produced
   increased levels of phenylpropanoids antioxidants to be used to promote
   the health of fruit and vegetable crops. Metabolomics is an important
   addition to the tools that are currently employed in genomics-assisted
   selection for crop improvement. Furthermore, because metabolites are
   last in the omics cascade (and are relatively close to the phenotype),
   they are a reliable tool for investigating abiotic stress responses in
   plants [[57]22]. In terms of N-deficiency tolerance, altered
   metabolites typically indicated that most of the amino acid content
   decreased, whereas most phenylpropanoids and organic acids increased in
   plant leaves [[58]23]. Furthermore, glutamate and aspartate levels were
   recognized as the best indicators of the stresses related to N
   limitations [[59]24].

   Although genetic improvements and fertilization practices are designed
   to enhance crop sources and sink capacities, whether the metabolites
   and metabolic pathways they affect are similar is not yet known. As
   such, in this study, we applied non-targeted metabolomic analysis to
   compare cultivars between different release years under N-deficient and
   adequate-N treatments. Our elucidation of the yield-increasing
   mechanisms of these two strategies based on metabolic profiles will
   assist with the generation of high-quality and high-yield crops through
   metabolic engineering and the precise application of exogenous
   substances in the future.

2. Materials and Methods

2.1. Experimental Site

   We conducted this study in Xinxiang, Henan(35°18′ N, 113°95′ E), in
   central China. During the wheat-growing seasons, the total
   precipitation and the mean temperature were 156.4 mm and 10.8 °C
   (2019–2020) and 149.5 mm and 9.7 °C, respectively (2020–2021). Prior to
   the investigation, we extracted soil samples from the upper 30 cm layer
   for chemical analyses. The soil type of the test site was fluvo-aquic
   and sandy, and the pH of the cultivated soil layer was 6.8, with an
   organic matter content of 15.3 g·kg^−1, which contained 1.1 g·kg^−1
   total N, 19.8 mg·kg^−1 available P, and 88.0 mg·kg^−1 available K.

2.2. Plant Materials and Experimental Design

   We used seven historical wheat cultivars released from the 1950s to the
   2010s in this study, all of which were cultivated as winter wheat in a
   large-scale area in the North China Plain over the 60-year periods
   ([60]Table 1). A total of 165 kg seeds ha^−1 were mechanically sown on
   3 October 2019 and 6 October 2020 with the dimensions of each plot
   being 6.0 × 8.0 m. We arranged the experiment with a randomized
   complete block design with three replications, and we used two N
   treatments: 0 and 180 kg N ha^−1. We applied N (urea) at two intervals,
   with 50% at the basal and 50% at the jointing stage (~160 days after
   sowing (DAS). We applied phosphorus (90 kg P[2]O[5] ha^−1) (as calcium
   superphosphate) and potassium (90 kg K[2]O ha^−1) (as potassium
   chloride) as the basal dose. We applied the basal fertilizer to the
   ground following manual broadcasting, and we applied N topdressing via
   side-dressing. We employed the wheat–maize rotation system in the field
   sites during the experimental period. The fertilization rate for the
   maize-growing season was the same as that for the wheat-growing season
   in each plot. Wheat typically requires three rounds of irrigation (each
   at 75 mm water), whereas maize requires only two rounds of irrigation
   (each at 100 mm water). We intensively controlled weeds, diseases, and
   insects during the entire growing season to avoid yield losses.

Table 1.

   Designation of seven selected wheat cultivars.
   Cultivar        Pedigree/Origin         Release Period
    ND2419  Rieti × Wilhelmina//Akagomughi     1950s
     BJ8         BM4 × Early Premium           1960s
     ZY1              St1472/506               1970s
     XY4          ZY4 × ZZ17 × 6609            1980s
    BN3217  Funo × NX5//XN39 × XN64 × YD34     1990s
     YM2             ZN16 × YM14               2000s
    BN207             Z16 × BN64               2010s
   [61]Open in a new tab

2.3. Plant Measurements and Analysis

2.3.1. Grain Quality and Yield Measurements

   At harvest, we measured the wheat grain yield for 6 m^2 areas in each
   plot using hand harvesting and machine threshing. We adjusted the
   grains to a moisture content of 0.13 g H[2]O g^−1 fresh weight. Once we
   air-dried the grains, we ground them into flour using a laboratory mill
   instrument (CD1, Chopin Technologies, Villeneuve-La-Garenne, France).
   Next, we measured the crude protein and wet gluten using the AACC
   No.39-11 and AACC No.38-12 standards, respectively. We measured the
   sedimentation value following the AACC No.58-61B standards, via the
   fungal falling number method (FN1000, Perten Instruments, Hagersten,
   Sweden).

2.3.2. Metabolite Extraction

   We accurately weighed 50 mg of fresh leaves and extracted the
   metabolites using a 400 µL methanol: water (4:1, v/v) solution. We
   allowed the mixture to settle at −20 °C and treated it with a
   high-throughput tissue crusher Wonbio-96c (Shanghai Wanbo Biotechnology
   Co., Ltd., Shanghai, China) at 50 Hz for 6 min, which was followed by
   vortexing for 30 s and ultrasound at 40 kHz for 30 min at 5 °C. We
   stored the samples at −20 °C for 30 min to precipitate the proteins.
   Following centrifugation at 13,000× g at 4 °C for 15 min, we carefully
   transferred the supernatant to sample vials for LC-MS/MS analysis.

2.3.3. LC-MS Conditions for Non-Targeted Metabolomic Analysis

   As part of the system conditioning and quality control process, we
   prepared a pooled quality control sample (QC) by mixing equal volumes
   of all samples. We prepared and tested the QC samples in the same
   manner as the analytic samples. This assisted with representing the
   entire sample set, which we injected at regular intervals (every eight
   samples) to monitor the stability of the analysis.

   We chromatographically separated the metabolites using a Thermo UHPLC
   system equipped with an ACQUITY UPLC HSS T3 column (100 mm × 2.1 mm
   i.d., 1.8 µm) (Waters, Milford, CT, USA). The mobile phases consisted
   of 0.1% formic acid in water (solvent A) and 0.1% formic acid in
   acetonitrile: isopropanol (1:1, v/v) (solvent B). We changed the
   solvent gradient according to the following conditions: From 0 to 3
   min, 95% (A): 5% (B) to 80% (A): 20% (B); from 3 to 9 min, 80% (A): 20%
   (B) to 5% (A): 95% (B); from 9 to 13 min, 5% (A): 95% (B) to 5% (A):
   95% (B); from 13 to 13.1 min, 5% (A): 95% (B) to 95% (A): 5% (B), from
   13.1 to 16 min, 95% (A): 5% (B) to 95% (A): 5% (B) for equilibrating
   the systems. The sample injection volume was 2 μL and we set the flow
   rate to 0.4 mL/min. We maintained the column temperature at 40 °C.
   During the analysis period, we stored all samples at 4 °C.

   We collected mass spectrometric data using a Thermo UHPLC-Q Exactive
   Mass Spectrometer equipped with an electrospray ionization (ESI) source
   operating in either positive or negative ion mode. We established the
   optimal conditions as follows: Aux gas heater temperature, 425 °C;
   sheath gas flow rate 50 psi; aux gas flow rate 13 psi; ion-spray
   voltage floating (ISVF), −2800 V in negative mode and 3500 V in
   positive mode; normalized collision energy, 20-40-60 V rolling for
   MS/MS. We acquired data acquisition in data-dependent acquisition (DDA)
   mode, and we performed the detection over a mass range of 70–1050 m/z.

2.3.4. Data Processing for Nontargeted Metabolomic Analysis

   Following UPLC-TOF/MS analyses, we transferred the raw data to
   Progenesis QI 2.3 (Nonlinear Dynamics, Waters, Milford, CT, USA), where
   we performed the identification of peaks and their alignment. The
   applied preprocessing resulted in a data matrix that encompassed the
   peak intensity, mass-to-charge ratio (m/z) values, and retention time
   (RT). We retained the metabolic elements if at least 80% were
   identified in any of the sample sets. Following filtering, we assigned
   minimal values to the metabolites of certain samples when they were
   below measurable levels, and we sum-normalized each metabolic element.
   We employed an internal standard for the QC of data to facilitate
   reproducibility, where we removed metabolic features with a QC relative
   standard deviation (RSD) of >30%. Subsequent to normalization and
   designation, we performed statistical analysis on log-transformed data
   to assess any major variations in metabolite values among equivalent
   groups. We determined the mass spectra of these metabolic elements by
   using the precise mass, MS/MS fragment spectra, and differences in
   isotope ratios by surveying dependable biochemical databases (e.g.,
   human metabolome database (HMDB) ([62]http://www.hmdb.ca/, accessed on
   24 November 2022) and the Metlin database
   ([63]https://metlin.scripps.edu/, accessed on 24 November 2022).
   Definitively, the mass tolerance between the quantified m/z values and
   the exact mass of the elements of interest was ±10 ppm. For MS/MS
   confirmation metabolites, we deemed only those with MS/MS fragment
   scores of >30 to be positively identified. Otherwise, we only
   tentatively assigned the metabolites.

2.4. Statistical Analysis

   We selected statistically significant metabolites among the sample
   groups with VIP values of >1 and p values of <0.05. We selected 28,534
   differential peaks, including 12,599 peaks in ESI+ and 15,935 peaks in
   ESI-. We summarized and mapped the differential metabolites between two
   groups into their biochemical pathways through metabolic enrichment and
   pathway analyses based on a database search (KEGG,
   [64]http://www.genome.jp/kegg/, accessed on 24 November 2022). We
   classified these metabolites according to the pathways in which they
   were engaged or the functions they performed. Enrichment analysis
   typically indicates whether or not a group of metabolites exists in a
   function node. The principle is that the annotation analysis of a
   single metabolite develops into an annotation analysis of a group of
   metabolites. We used scipy. stats (Python packages)
   ([65]https://docs.scipy.org/doc/scipy/, accessed on 24 November 2022)
   to identify statistically significantly enriched pathways using
   Fisher’s exact test.

3. Results

3.1. Genetic Improvements and N Application for Increased Cultivar Quality
and Yield

   The results of two-way ANOVAs showed that the grain yield, crude
   protein, wet gluten, and sedimentation value were significantly
   affected by cultivar (C) and nitrogen (N) in these two growing seasons
   ([66]Figure 1A–H). Additionally, the effects of C and N interactions on
   grain yield and quality were highly significant. For both growing
   seasons, the cultivar yields increased with progressing release years.
   Under the -N treatments, the grain yields of newer cultivars were
   significantly higher than those of older cultivars ([67]Figure 1A,B).
   The yields from the 1950s–1980s cultivars increased over time during
   the two growing seasons, while those from the 1980s–2010s did not
   change (2019–2020) or even decreased (2020–2021). Following N
   fertilization, the grain yields of all cultivars significantly
   increased at rates that ranged from 33.8% to 65.1% (2019–2020) and
   19.1% to 81.6% (2020–2021). Distinct from the modified yield,
   grain-quality-related indicators did not exhibit a uniform trend
   ([68]Figure 1C–H). The crude protein and sedimentation values of the
   1980s cultivar were significantly higher than those of other cultivars.
   Notably, the application of N fertilizer still markedly improved the
   grain quality, crude protein, sedimentation value, and wet gluten by
   19.6%, 20.5%, and 44.7%, respectively, on average during the two
   growing seasons.

Figure 1.

   [69]Figure 1
   [70]Open in a new tab

   Yields and quality of wheat cultivars from different release years
   under −N and +N treatments from 2019–2020 (A,C,E,G) and 2020–2021
   (B,D,F,H).

   From the 1950s to the 2010s, the agronomic plant heights and panicle
   numbers steadily decreased and the grain numbers and weights increased
   ([71]Table 2). The results of two-way ANOVA indicated that all
   agronomic traits were affected by C or N, and we found significant C ×
   N interactions in the number of panicles and grains per panicle.
   Following N fertilization, plant heights and panicle numbers
   significantly increased, and the responses of grain numbers and weights
   varied for the cultivars of different eras. The grain weights of the
   1950s, 1960s, and 1980s were considerably increased by the addition of
   N, whereas we noted few changes in the other cultivars.

Table 2.

   Agronomic traits of wheat cultivars from different years of release
   under −N and +N treatments. Significant differences between two N
   treatments for same cultivar are indicated by ns, no significance; * p
   < 0.05; ** p < 0.01. Level of significance in univariate ANOVA (two-way
   analysis of variance) is denoted by ns, no significance; ** p < 0.01.
   Release Year N Treatments Plant Height (cm) No. of Panicles (×10^4
   m^−2) Grains Per Panicle Grain Weight (mg)
   2019–2020
   1950s −N 107.5 377.7 32.7 35.1
   +N 116.7 ** 527.0 ** 30.6 ^ns 38.3 **
   1960s −N 115.9 343.7 36.1 31.7
   +N 129.0 ** 491.7 ** 42.4 * 33.2 *
   1970s −N 86.1 334.7 38.9 42.1
   +N 95.9 ** 530.3 ** 36.6 * 43.1 ^ns
   1980s −N 99.9 364.0 39.8 42.8
   +N 109.0 ** 424.7 ** 41.4 ^ns 46.6 **
   1990s −N 86.4 369.7 42.5 42.1
   +N 88.3 ^ns 486.7 ** 43.9 ^ns 42.2 ^ns
   2000s −N 77.4 339.3 40.8 45.4
   +N 85.0 * 440.0 ** 45.0 ** 45.7 ^ns
   2010s −N 75.4 273.3 43.7 46.9
   +N 80.8 * 394.0 ** 47.5 ** 47.7 ^ns
   Cultivar (C) ** ** ** **
   Nitrogen (N) ** ** ** **
   C × N ns ** ** ns
   2020–2021
   1950s −N 110.7 401.0 32.3 36.2
   +N 115.3 * 558.3 ** 30.7 ^ns 39.8 *
   1960s −N 118.5 381.0 33.7 29.0
   +N 131.5 ** 536.7 ** 35.8 * 34.2 **
   1970s −N 84.0 320.3 38.0 44.0
   +N 95.2 ** 544.0 ** 34.9 * 43.8 ^ns
   1980s −N 101.9 358.7 40.0 43.1
   +N 108.6 ** 421.3 ** 40.5 ^ns 46.3 *
   1990s −N 82.6 379.3 40.9 42.3
   +N 85.3 * 507.0 ** 44.2 ** 41.4 ^ns
   2000s −N 79.2 326.0 41.8 44.2
   +N 81.7 ^ns 441.0 ** 46.3 ** 45.6 ^ns
   2010s −N 74.0 254.7 44.7 46.6
   +N 78.2 * 376.0 ** 51.4 ** 48.1 ^ns
   Cultivar (C) ** ** ** **
   Nitrogen (N) ** ** ** **
   C × N ns ** ** ns
   [72]Open in a new tab

3.2. Effects of Genetic Improvement and N Application on Metabolic Profiles

   The PLS-DA plot suggested that the metabolic profiles differed between
   the various wheat cultivars, with the results indicating that leaf
   metabolites were clustered by the release era ([73]Figure 2). The
   positive and negative metabolites were significantly different between
   the cultivars under the in −N and +N treatments. Furthermore, the
   loading plot from the analysis showed a clear separation in metabolites
   between the -N and +N groups for both positive and negative modes
   ([74]Figure 3).

Figure 2.

   [75]Figure 2
   [76]Open in a new tab

   Partial least-squares–discriminant analysis (PLS-DA) of leaf metabolic
   profiles of different years of released cultivar under −N (A,B) or +N
   (C,D) treatments. Positive and negative modes are shown.

Figure 3.

   [77]Figure 3
   [78]Open in a new tab

   Partial least-squares–discriminant analysis (PLS-DA) of leaf metabolic
   profiles between the +N and −N treatments: positive (A) and negative
   (B) modes.

3.3. Overview of Altered Metabolites under Genetic Improvement and N
Application Groups

   Compared with the oldest cultivar (the 1950s), 78 metabolites in the
   1980s cultivar were upregulated and 93 were significantly downregulated
   ([79]Table 3). The main types of regulated metabolites were lipids and
   lipid-like molecules (42.50%), which indicated the ratio of these
   substances to the total differential metabolites. Additional
   constituent metabolites included phenylpropanoids and polyketides
   (20.62%), organic oxygen compounds (16.12%), and organoheterocyclic
   compounds (9.38%). Compared with the 1980s cultivar, 33 metabolites in
   the 2010s cultivar were upregulated and 42 were significantly
   downregulated. The main types of regulated metabolites were lipids and
   lipid-like molecules (30.14%), followed by phenylpropanoids and
   polyketides (27.40%), organic oxygen compounds (19.18%), and organic
   acids and derivatives (8.22%). We found 36 shared differential
   metabolites between the 2010s vs. 1980s and 1980s vs. 1950s groups,
   where most of them were attributed to lipids and lipid-like molecules,
   phenylpropanoids, and polyketides ([80]Figure 4A; [81]Tables S1 and
   S2).

Table 3.

   Statistics of differential metabolites between cultivar and N treatment
   groups.
   HMDB Superclass Group
   Cultivar N Treatment (+N vs. −N)
   1980s vs. 1950s 2010s vs. 1980s 1950s 1980s 2010s
   Lipids and lipid-like molecules 68 22 4 19 22
   Phenylpropanoids and polyketides 33 20 6 4 10
   Organoheterocyclic compounds 15 6 5 7 2
   Organic oxygen compounds 29 14 1 20 7
   Organic acids and derivatives 8 6 2 9 5
   Nucleosides, nucleotides, and analogues 1 1 1 0 1
   Mixed metal/non-metal compounds 1 1 0 0 0
   Lignans, neolignans and related compounds 1 0 1 0 1
   Benzenoids 4 3 0 0 1
   Others 11 2 3 9 5
   Total 171 75 23 68 54
   [82]Open in a new tab

Figure 4.

   [83]Figure 4
   [84]Open in a new tab

   Venn diagram of differential metabolites in cultivar group (A) and N
   treatment group (B).

   Distinct from genetic improvements, the application of N fertilizer
   regulated fewer metabolites. We identified 23 altered metabolites (16
   upregulated and 7 downregulated) for the 1950s cultivar, 68 (20
   upregulated and 48 downregulated) for the 1980s cultivar, and 54 (18
   upregulated and 36 downregulated) for the 2010s cultivar ([85]Table 2).
   The main types of regulated metabolites for the +N vs. −N groups for
   the 1950s cultivar included phenylpropanoids and polyketides (30.00%),
   organoheterocyclic compounds (25.00%), lipids and lipid-like molecules
   (20.00%), and organic acids and derivatives (10.00%). The primary types
   of regulated metabolites for the +N vs. −N groups for the 1980s
   cultivar were organic acids and derivatives (33.9%), lipids and
   lipid-like molecules (32.20%), organic acids and derivatives (15.25%),
   and organoheterocyclic compounds (11.86%). The main types of regulated
   metabolites for the +N vs. vs. −N group in the 2010s cultivar were
   lipids and lipid-like molecules (44.90%), phenylpropanoids and
   polyketides (20.41%), organic oxygen compounds (14.29%), and organic
   acids and derivatives (10.20%). We identified only one shared
   differential metabolite (5′-carboxy-gamma-chromanol) between the above
   three N treatment groups ([86]Figure 4B; [87]Tables S3–S5).

3.4. Altered Metabolic Pathways in Genetic Improvement and N Application
Groups

   The results of our metabolic pathway analyses of the identified
   differential metabolites indicated differences in one carbon pool via
   folate and tryptophan metabolism between the 1980s and 1950s cultivars,
   and only one pathway difference in aflatoxin biosynthesis between the
   2010s and 1980s cultivars ([88]Figure 5A,B).

Figure 5.

   [89]Figure 5
   [90]Open in a new tab

   KEGG enrichment maps of different metabolites in 1980s vs. 1950s groups
   (A) and 2010s vs. 1980s groups (B). * indicate enrichment with
   significance at 0.05 level.

   We identified two KEGG pathways (anthocyanin and flavonoid
   biosynthesis) enriched by differential metabolites following the
   addition of N fertilizer in the 1950s cultivar; two enriched pathways
   (arginine and proline metabolism and one carbon pool by folate) in the
   1980s cultivar; and four enriched pathways (pyrimidine metabolism,
   phenylpropanoid biosynthesis, flavonoid biosynthesis, and stilbenoid,
   diarylheptanoid, and gingerol biosynthesis) in the 2010s cultivar
   ([91]Figure 6A–C).

Figure 6.

   [92]Figure 6
   [93]Open in a new tab

   KEGG enrichment maps of different metabolites between +N and −N
   treatments of 1950s cultivar (A), 1980s cultivar (B), and 2010s
   cultivar (C). *and ** indicate enrichment with significance at the 0.05
   and 0.01 levels, respectively.

3.5. Relationships between Metabolic Profiles and Quality and Yield in
Genetic Improvement and N Application Groups

   A heatmap showed that seven lipids and lipid-like molecules, three
   organic acids and derivatives, two phenylpropanoids and polyketides,
   one organic oxygen compound, and one organoheterocyclic compound of the
   VIP top 30 differential metabolites positively correlated with yields
   in the 1980s and 1950s cultivar groups. Furthermore, four lipids and
   lipid-like molecules, three organic oxygen compounds, two benzenoids,
   two phenylpropanoids and polyketides, one organic acid and derivative,
   and one other negatively correlated with yield. Except for phenyllactic
   acid, graecnin E, trihydroxyoxane, and trehalose 6-phoshate, most of
   the differential metabolites related to yields were related to quality
   indicators ([94]Figure 7A). When the cultivar was updated from the
   1980s to the 2010s, we found no substances that affected the wet gluten
   content. However, three phenylpropanoids and polyketides, one lipid and
   lipid-like molecule, one organic acid and derivative, one organic
   oxygen compound, and one organoheterocyclic compound of the VIP top 30
   differential metabolites positively correlated with yields, crude
   proteins, and sedimentation values. Furthermore, two lipids and
   lipid-like molecules, two organic oxygen compounds, and one benzenoid
   negatively correlated with yield, crude protein, and sedimentation
   values ([95]Figure 7B). Additionally, a small number of differential
   metabolites was related to either grain yield or quality.

Figure 7.

   [96]Figure 7
   [97]Open in a new tab

   Correlation between metabolics and grain yield and quality in 1980s vs.
   1950s groups (A) and 2010s vs. 1980s groups (B). *, ** and *** indicate
   the correlation is significant at 0.05, 0.01 and 0.001 levels,
   respectively.

   The heatmap revealed many more differential metabolites of the VIP top
   30 in the +N vs. −N groups of the 1950s cultivar, which positively
   correlated with grain yields or quality, whereas more differential
   metabolites in the +N vs. −N groups of the 1980s and 2010s cultivars
   negatively correlated with grain yield or quality ([98]Figure 8A–C). In
   the 1950s cultivar, two organoheterocyclic compounds; one nucleoside,
   nucleotide, and analogue; and one other of the VIP top 30 differential
   metabolites positively correlated with grain yield and quality
   following the application of N, whereas two phenylpropanoids and
   polyketides, one organoheterocyclic compound, and one lipid and
   lipid-like molecule negatively correlated with grain yield and quality
   ([99]Figure 8A). In the 1980s cultivar, only one organic oxygen
   compound positively correlated with grain yield and quality following
   the addition of N, whereas one organic oxygen compound, one
   phenylpropanoid and polyketide, and one other were negatively
   correlated with grain yield and quality. Furthermore, the majority of
   differential metabolites negatively correlated with yield but they were
   unrelated to grain quality ([100]Figure 8B). In the 2010s cultivar,
   three lipid-like molecules, two organic acids and derivatives, and one
   other positively correlated with grain yield and quality after the
   addition of N, whereas five lipid-like molecules, two organic oxygen
   compounds, and two others negatively correlated with grain yield and
   quality ([101]Figure 8C).

Figure 8.

   [102]Figure 8
   [103]Open in a new tab

   Correlation between metabolics and grain yield and quality in the +N
   vs. −N groups of 1950s (A), 1980s (B), and 2010s (C) cultivars. *, **
   and *** indicate the correlation is significant at 0.05, 0.01 and 0.001
   levels, respectively.

4. Discussion

4.1. Effects of Cultivar Improvements on Wheat Yield and Quality Based on
Metabonomics

   In this study, our results revealed that the modern cultivars performed
   better than their older counterparts under both N fertilization levels.
   Over a period of 60 years, genetic improvements increased crop yields
   by up to 98.2% (2019–2020) and 96.7% (2020–2021), whereas the
   application of N fertilizer increased yields by up to 65.1% (2019–2020)
   and 81.6% (2020–2021) ([104]Figure 1A,B). The yield increases achieved
   through genetic improvements were primarily attributed to the
   continuously increased spikelet numbers and grain weights, which were
   accompanied by a decrease in panicle numbers ([105]Table 1). These
   results are akin to those of Giunta et al. [[106]7], with the only
   difference being that the grain weight of a modern wheat cultivar in
   Italy was 10% less than that of the old cultivar. Furthermore, our
   results align well with those from a study on rice (both rice and wheat
   belong to Poaceae) by Zhu et al. [[107]25].

   We investigated the metabolic profiles of different wheat cultivars
   based on an untargeted metabolomics approach. The notable distinction
   between the three clusters under the -N or +N treatments suggested a
   significant difference in the metabolic composition between groups
   ([108]Figure 2). Additionally, the 1980s vs. 1950s groups produced more
   than twice as many differential metabolites as the 2010s vs. 1980s
   groups ([109]Table 3). This meant that changes in the attributes of the
   cultivars from the 1950s to 2010s were more prominent during the
   initial 30 years in northern China.

   Metabolites are closely related to crop yield and quality, and
   metabolomic strategies can be applied to improve crop function, enhance
   nutritional quality, and increase grain yield [[110]26,[111]27]. We
   found significant disparities in the metabolic elements between the
   1980s and 1950s cultivars, primarily focused on lipids and lipid-like
   molecules, phenylpropanoids and polyketides, organic oxygen compounds,
   organic oxygen compounds, and organic acids and derivatives ([112]Table
   3 and [113]Table S1). Most organic acid levels were higher in the 1980s
   cultivar, whereas most lipids (prenol lipids and fatty acyl) were
   higher in the 1950s cultivar. Organic acids are generally intermediate
   respiration and photosynthesis products in higher plants, which are
   essential for ammonia assimilation and amino acid synthesis [[114]23].

   The results of the KEGG pathway enrichment analysis confirmed that the
   down-regulation of N-acetylserotonin in amino acid metabolism delayed
   tryptophan degradation ([115]Figure 5). Thus, more peptides accumulated
   in the 1980s cultivar, among which arginyl-proline,
   leucyl-phenylalanine, and threoninyl-proline were significantly
   positively related to grain yield and quality ([116]Figure 7).
   Typically, these small peptides play critical signaling roles in plants
   [[117]28]. Of the many decreased lipids levels in the 1980s cultivar,
   four lipids (methoxyestrone, bornanone glucoside, capsidio, and
   curcumadiol) were significantly negatively related to grain yields and
   quality. No relevant report suggests that methoxyestrone and bornanone
   glucoside impacted plant growth; however, capsidio and curcumadiol may
   improve the protective enzyme activities and fungal disease resistance
   of plants [[118]29,[119]30].

   Although we found fewer differences in the metabolic elements between
   the 2010s and 1980s cultivars, we noted more similarities in the
   tillers, plant height, yield, quality, and other traits. The levels of
   the majority of organic acids were higher in the 1980s cultivar,
   whereas most of the lipid levels (prenol lipids and fatty acyls) were
   higher in the 2010s cultivar. Among these organic acids, dynorphin B
   was significantly positively related to grain yields and quality. For
   this substance, researchers previously found that it had an analgesic
   effect in animals; however, it had not been reported in plants
   [[120]31]. The KEGG pathway results showed that the differential
   metabolites were enriched in aflatoxin biosynthesis, and we found some
   aflatoxin G in the 2010s cultivar ([121]Figure 5). As a potent
   carcinogen, it might also inhibit the growth of plants, though no
   inhibitory mechanism has been reported to date [[122]32].

4.2. Effects of N Fertilizer Application on Wheat Yield and Quality Based on
Metabonomics

   Higher wheat yield was premised on the accumulation of more aboveground
   dry matter, whereas adequate N absorption was deemed to be critical for
   its accumulation [[123]33,[124]34]. In this study, further increasing
   the yield of modern wheat cultivars through genetic improvements in the
   absence of N fertilizers appears to be difficult. However, modern
   cultivars are increasingly dependent on N for high grain yields
   ([125]Figure 1). Following the application of N, the increased panicle
   numbers and grain weight production increased in the 1950s and the
   1980s, whereas the yield increases of the 2010s cultivar were caused by
   the increased panicle number and grain number per panicle ([126]Table
   1). The increased rate of productive tillers induced by the addition of
   N was higher in the old cultivars than in the modern cultivar. However,
   most small tillers in the old cultivar had fewer grains; thus, we
   observed a reduced grain number per panicle under the +N treatment.

   We found a more distinct separation between the -N and +N treatments in
   wheat leaves via PLS-DA, the results of which suggested an apparent
   metabolic alteration induced by N deficiencies. Similar findings were
   also reported in the N-deficient leaves of tomato [[127]35], tea
   [[128]23], and rice [[129]36]. In the present study, the number of
   differential metabolites induced by the application of N was far
   smaller than that caused by genetic improvements ([130]Table 3).
   Because metabolites are useful predictors of quantitative traits
   [[131]19], this finding indicated that genetic improvements more
   strongly impacted agronomic traits than the application of N. Following
   N fertilization, the numbers of differential metabolites of the 1980s
   cultivar were the highest, followed by those of the 2010s cultivar,
   with the 1950s cultivar producing the fewest. Carboxy-chromanol was the
   only substance that was upregulated after N application for all wheat
   cultivars, which can suppress colon tumors and colitis in animals;
   however, no report has been published on its effects on plants
   [[132]37,[133]38].

   For the 1950s cultivar, most differential metabolites were upregulated
   following the application of N, with only a few differential
   metabolites being down-regulated ([134]Table S3). Among them,
   delphinidin was enriched in two pathways, anthocyanin and flavonoid
   biosynthesis ([135]Figure 6). N-deficiencies inhibited protein
   synthesis, which promoted the increased accumulation of carbohydrates
   for anthocyanin synthesis [[136]39]. For the 1980s and 2010s cultivars,
   the majority of differential metabolites were downregulated after the
   application of N, particularly for carbohydrates and carbohydrate
   conjugates, lipids and lipid-like molecules, and phenylpropanoids and
   polyketides. The accumulation of carbohydrates and lipids in
   N-deficient plants increased osmotic pressure, which resulted in the
   over-accumulation of reactive oxygen species [[137]23]. In the present
   study, the KEGG pathway enrichment analysis confirmed that the
   downregulation of chlorogenic acid in phenylpropanoid biosynthesis
   delayed the degradation of phenylalanine ([138]Figure 8). The
   phenylpropanoid biosynthesis pathway is involved in plant disease
   resistance and immunity [[139]40,[140]41].

5. Conclusions

   Both genetic improvements and the application of N enhanced the yield
   and quality of wheat. From the 1950s to 2010s cultivars, the yield
   increase achieved through genetic improvement ranged from −1.9% to
   96.7%, whereas that achieved through the application of N ranged from
   19.1% to 81.6%. Based on the results of an untargeted metabolomics
   approach, we found that genetic improvements more strongly induced
   metabolic modification than N fertilization. Compared with the 1950s
   cultivar, the 1980s cultivar showed higher relative abundances of most
   organic acids, and lower relative abundances of most lipids in wheat
   leaves. Of the subsequent cultivar improvements, the 2010s cultivar
   showed upregulated relative abundances of most lipids and downregulated
   relative abundances of most organic acids and organoheterocyclic
   compounds compared to the 1980s cultivar. Differentially, essentially
   all of the differential metabolites were upregulated in the 1950s
   cultivar, whereas many differential metabolites were downregulated,
   particularly for carbohydrates and carbohydrate conjugates, lipids and
   lipid-like molecules, and phenylpropanoids and polyketides. These
   findings strengthen our understanding of the molecular mechanisms that
   underlie the differences between genetic improvements and N
   applications to increase wheat grain yield and quality.

Acknowledgments