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Review

Breeding for Higher Yields of Wheat and Rice through Modifying Nitrogen Metabolism

by
Pornpipat Kasemsap
* and
Arnold J. Bloom
Department of Plant Sciences, University of California at Davis, Mailstop 3, Davis, CA 95616, USA
*
Author to whom correspondence should be addressed.
Plants 2023, 12(1), 85; https://doi.org/10.3390/plants12010085
Submission received: 23 November 2022 / Revised: 21 December 2022 / Accepted: 21 December 2022 / Published: 23 December 2022
(This article belongs to the Collection Feature Papers in Plant Physiology and Metabolism)
Versions Notes

Abstract

:
Wheat and rice produce nutritious grains that provide 32% of the protein in the human diet globally. Here, we examine how genetic modifications to improve assimilation of the inorganic nitrogen forms ammonium and nitrate into protein influence grain yield of these crops. Successful breeding for modified nitrogen metabolism has focused on genes that coordinate nitrogen and carbon metabolism, including those that regulate tillering, heading date, and ammonium assimilation. Gaps in our current understanding include (1) species differences among candidate genes in nitrogen metabolism pathways, (2) the extent to which relative abundance of these nitrogen forms across natural soil environments shape crop responses, and (3) natural variation and genetic architecture of nitrogen-mediated yield improvement. Despite extensive research on the genetics of nitrogen metabolism since the rise of synthetic fertilizers, only a few projects targeting nitrogen pathways have resulted in development of cultivars with higher yields. To continue improving grain yield and quality, breeding strategies need to focus concurrently on both carbon and nitrogen assimilation and consider manipulating genes with smaller effects or that underlie regulatory networks as well as genes directly associated with nitrogen metabolism.

1. Introduction

Balancing crop nitrogen and carbon status under changing environmental conditions is essential for sustaining agricultural productivity and food security. Nitrogen constitutes 1 to 2% of plant dry biomass, yet plants allocate a disproportionate amount of their energy to convert inorganic nitrogen forms, especially nitrate (NO3−) and ammonium (NH4+), into organic compounds [1]. As much as 50% of total plant protein may be ribulose-l,5-bisphosphate carboxylase/oxygenase (rubisco), the enzyme that initiates plant assimilation of CO2 into organic carbon through C3 carbon fixation [2]. Consequently, plant organic nitrogen and organic carbon are inextricably linked [3]. Rice (Oryza sativa L.) breeding has succeeded in increasing both grain yield and grain protein concentration in recent decades [4]. In contrast, long-term wheat (Triticum aestivum L.) breeding has achieved incremental biomass yield gain, but at the loss of grain protein content over time [5]. Plant breeders therefore actively seek to achieve two sometimes opposing goals, maximization both food productivity and quality.
Articles about breeding strategies to improve yield often discuss crop ideotype, outlining and dissecting desirable traits with the potential to achieve the highest theoretical yield or most rapid progress in genetic gains (for example, [6,7,8,9,10,11]). This review offers commentary presented in four sections: Process, Progress, Prospects and Puzzles. First, we briefly discuss crop inorganic nitrogen uptake, assimilation, and mobilization, topics for which a plethora of reviews already exist (for example, [12,13]). Second, we evaluate recent successful breeding endeavors involving genes within the nitrogen pathways that improve yield, using the framework of yield component analyses. Third, we present key trends among 40 validated genes that enhance crop yield. Highlighted are genes that influence tiller number, flowering time, and NH4+ assimilation. Lastly, we conclude by identifying areas for further research such as homologs across species, responses to different inorganic nitrogen forms, and complexities of natural variation and epistasis.

2. Process—Reaping What We Sow: How Soil Nitrogen Makes Its Way onto Our Plates

Plants acquire most of their nitrogen, both organic and inorganic forms, from soil, but reliance on each form varies greatly over time, with location, and under different environmental conditions [14]. Soil microorganisms mineralize organic nitrogen into NH4+, which subsequently becomes oxidized into NO3− through nitrification [15]. Plants compete with soil microbes for NH4+, a form which also serves as a crucial microbial energy source [16]. In temperate aerobic agricultural soils, microbial activities rapidly convert most soil nitrogen into NO3−, and so NO3− remains the dominant soil inorganic nitrogen compound available to crops [16,17].
Plant nitrogen acquisition relies on a well-coordinated network of transporters [13]. Nitrate transporters are among the most extensively studied groups of proteins and include low and high affinity systems that cover a large range of concentrations in soil; they also have additional functions beyond NO3− transport [4]. Ammonium transporters are considered high affinity systems because they operate under low NH4+ concentrations [4]. The primary inorganic nitrogen assimilation pathway involves several reactions: nitrate reductase (NR) catalyzes NO3− reduction into nitrite (NO2−), nitrite reductase (NiR) catalyzes nitrite (NO2−) reduction into ammonium (NH4+), and the concurrent actions of glutamine synthetase/glutamate synthase (GS/GOGAT) catalyze the incorporation of NH4+ into amino acids [18]. The resulting organic nitrogen compounds are transported, remobilized, and re-assimilated in different organs according to sink demand as a plant develops [4]. As plants mature and reach a reproductive stage, nitrogen compounds that have accumulated throughout vegetative stages are directed toward seeds, the organs vital to species survival and the harvestable part for most crops [4].
Our major focus here is wheat and rice for multiple reasons. First, these crops are the two top sources of plant protein that we consume daily according to the Food and Agriculture Organization of the United Nations (FAO) [19] (Table 1). Relative reliance on these two crops as a major protein source varies across geographical regions. Wheat contribution to human protein intake is dominant in Northern Africa (38%), Central Asia (38%), Southern Asia (26%), Western Asia (39%), and Europe (22–29%). Rice prevails in South-eastern Asia (34%), Southern Asia (21%), and Micronesia (18%). Second, wheat and rice are both C3 plants belonging to the Poaceae family. Such relatedness may facilitate the transfer of knowledge between these two closely related species, although the genome of hexaploid wheat is 40 times larger than that of rice [20]. Third, as model species and major food crops, they both have been the subject of extensive research extending over a broad range of production areas across diverse environmental conditions worldwide [21]. Lastly, under current cultivation practices, wheat and rice may have adapted to different habitats [22], especially to distinct forms of inorganic nitrogen. Wheat is grown in aerobic soils, dominated by NO3−, whereas rice is grown usually under hypoxic conditions with a relatively high NH4+ presence in the root zone. Understanding how major food crops adapt to different forms of nitrogen should highlight nutrient management strategies to improve grain yield and quality. Three major components contribute to yield of small grain crops: number of tillers, number of grains per tiller (or grains per spike), and grain weight [23]. Number of grains per tiller may be further divided into number of panicles (or spikelets) and number of grains per panicle (or spikelet). Whereas tiller development can be influenced significantly by changes in the environment, grain characteristics are highly heritable [23,24]. Grain number and grain yield are positively correlated with crop nitrogen content [21]. Crops absorption of NO3− and NH4+ from soils and assimilation into organic forms reaches a peak at anthesis [25]. During grain production, plants remobilize stored organic nitrogen compounds and translocate them to seeds [26]. Nitrogen supply from before planting until anthesis is more strongly related to vegetative growth and yield potential, while nitrogen application post-anthesis is more strongly related to improved protein content and grain quality [25,27]. Photosynthesis, a process in which nitrogen-rich compounds play a major role, contributes biomass to fill in grain weight [21]. In other words, nitrogen is fundamental to all processes that determine final grain yield [11]. Therefore, optimizing nitrogen acquisition throughout crop development is crucial for attaining maximum yield potential.

3. Progress—Common Breeding Strategies Are Limited to Regulating Expression of Few Genes

Plant breeders achieve genetic gain in breeding populations over generations by selecting and retaining genetic materials with targeted characteristics and superior performance. A more thorough understanding of the molecular biology and genetic basis of specific traits facilitates the rapid development of more desirable genotypes, especially for traits that are controlled by a single or few loci with large effects. Yet, improving complex traits like yield and nitrogen responses remains challenging.
Will breeding for improved nitrogen uptake and assimilation also increase yield? While yield improvement can arise from factors affecting yield components besides increased nitrogen use efficiency (NUE), breeding for this trait should lead to increased biomass production and grain yield [28,29]. Nonetheless, breeding programs for yield seldom monitor nitrogen responses [30,31,32,33], and modern cultivars with higher yield demonstrate little improvement in NUE [34].
The genetic basis underlying desirable phenotypes for grain yield and quality often remain obscure, despite genetic gains through selection. For example, in Green Revolution varieties, the genetic variants and mechanisms responsible for the short stature and increased harvest index that underpin the yield boost were identified only several decades after the release of improved cultivars [35]. In rice, the recessive loss-of-function mutation of Semi-Dwarf 1 (SD1) impairs an oxidase enzyme in the synthesis pathway of gibberellin, a key hormone promoting height, whereas in Green Revolution-derived varieties of wheat, mutations of Reduced Height 1 (RHT-1) encode modified proteins that also diminish height, but are insensitive to gibberellin-induced degradation [36,37,38,39]. Dwarfing genes improve yield through several mechanisms that act in concert to both diminish height and significantly increase biomass partitioning to the grain [36]. High harvest index, the fraction of biomass allocated to harvestable organs, is known to be strongly associated with high crop nitrogen status [40]. Unfortunately, many Green Revolution phenotypes, regardless of the mechanisms responsible for decreased gibberellin responses, also limit crop responses to nitrogen [41,42]. Plants with a dwarfing gene often have slower nitrogen uptake [42] and nitrogen accumulation relative to dry matter accumulation after anthesis, thereby decreasing NUE on a grain biomass basis in the field [41]. Insensitivity to increased nitrogen supply may be beneficial because the absence of nitrogen-promoted stem elongation makes plants more resistant to lodging [42], although at the high cost of requiring additional nitrogen fertilizer to maintain adequate yield. This case study from Green Revolution varieties underscores the challenge of improving yield through modifying nitrogen metabolism.
Generally, attempts to improve yield also alter rates or paths of metabolite production [43]. In particular, the enhanced harvest index of widely grown Green Revolution varieties diverts more biomass into harvestable grains. Nonetheless, assuming we have not reached the limits of biomass production, we may coordinate source vs. sink balance and continue to allocate additional crop assimilation of carbon and other nutrients toward yield [44]. Although efforts to increase crop source strength in terms of light capturing efficiency have been long underway [45], this goal seems elusive unless we address water and nutrient limitations [46,47,48]. Greater emphasis on enhancing nitrogen accumulation upon which biomass production depends may prove more effective in increasing yield in the near future [46,48]. With more extensive knowledge about the genetics of the underlying traits and advanced breeding tools, we could perhaps make even faster progress if we target both enhanced carbon and nitrogen assimilation concurrently.
An extensive body of literature is now available about the major transporters and enzymes associated with nitrogen assimilation and remobilization throughout crop growth cycles [4,13,30,49]. Characterized and cloned are key genes that govern metabolic pathways, but successful breeding applications for yield improvement that involve these genes are few, especially those that have reached the stage of commercial field trials [12].
Here, we have tabulated 40 genes that influence nitrogen metabolic pathways and improve grain yield (Table 2). Among these, regulation of gene expression seems to be the most successful approach for translating improved nitrogen metabolism into higher yields. Overexpression of genes [50] is the most common approach. Less common is knocking out [51] or silencing the genes of interest with small interfering RNA (RNAi) [52] or Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR/Cas9) [53] that precisely targets specific genomic regions. Relatively few studies have employed conventional breeding methods and, thus, have avoided transgenic means for introgression or incorporation of a functional allele into a breeding population. Progress in rice overall has been more rapid than in wheat [54]. Below are four different categories of genes involved in nitrogen metabolism that recent breeding efforts have manipulated to improve yield.

3.1. Nitrogen Transporters (17 Genes)

Modern phylogenetic studies classify major families of nitrogen transporters in land plants based on their substrate: nitrate, ammonium, or peptides [99]. Most characterizations of transporters are in Arabidopsis and rice [100]; relatively limited information is available for transporters in wheat [30,101,102]. Nitrate transporters (11 genes) have received more attention and their potential for yield improvement have been evaluated more thoroughly than transporters of ammonium and organic nitrogen [13,33]. Perhaps this derives from NH4+ being a nitrogen source that only dominates in a few agricultural production systems and from the ability of its counterpart NH3 to move freely through membranes following electrochemical gradients [1]. Ammonium transporters (2 genes) may also prove to be more elusive as a target for yield improvement because of the potential for toxicity from excessive accumulation of free NH4+ in tissues as discussed below [33,103]. Nonetheless, coupling NH4+ uptake with assimilation by concurrent modification of AMT1;2 and GOGAT can drive yield improvement [69].
Modification of amino acid transporters (AAP, 4 genes), despite receiving less attention than that of NO3− transporters [12], is another effective strategy for increasing grain yield. These transporters, in contrast to those that transport NO3− or NH4+, are key players in remobilizing assimilated organic nitrogen compounds, although their exact functions remain largely unknown in cereals [104]. Organic nitrogen transport within plants directly relates to grain nutritional quality at maturity [26]. Of particular interest is that variation in the promoter regions across germplasm suggest tight expression regulation and local adaptation that may help plants cope with fluctuations in soil nitrogen gradients [75,76,77].
Overall, while we have some understanding of how transporters contribute to uptake and transport of each nitrogen form across membranes and might improve plant nitrogen acquisition, modification of these transporters has had limited success in crop yield enhancement [33].

3.2. Nitrogen Assimilatory Enzymes (5 Genes)

Assimilation of inorganic into organic nitrogen in plants is well-regulated at transcriptional, translational, and post-translational levels [105,106]. The enzymes GS and GOGAT are central to nitrogen metabolism, but attempts to alter yield by modifying genes coding for these enzymes have achieved only little success. Previous modification to GS1 increased nitrogen partitioning to grain and nitrogen harvest index, but not vegetative yield nor overall shoot nitrogen accumulation [107]. Failure to successfully modify GS1 on its own may derive from the critical functions for which this gene is responsible [108]. By contrast, modifying GS2 can lead to wheat yield improvement in stressful environments [71]. Alteration to GOGAT expression to boost yield was only achieved through changing expression levels of transcription factors upstream of the enzyme (see discussion below). Thus, successful breeding applications coupled GOGAT with changes to ammonium transporter AMT1;2 [69] or simultaneously modulated GS1 and GS2 [70]. Because GS1 and GS2 are involved in crop growth at different developmental stages [109,110], selecting the appropriate developmental time to express each of these enzymes was critical for a positive result [70].

3.3. Nodule INception-like Proteins That Sense NO3− and Regulate Downstream Genes (3 Genes)

Legumes when associated with certain bacteria can generate organic nitrogen from dinitrogen gas N2 in air, a process named symbiotic nitrogen fixation [111]. Nodule INception (NIN) genes govern legume root nodule initiation and symbiotic nitrogen fixation [112]. NIN-like proteins (NLPs) that are homologs to NINs found in non-leguminous crops have critical roles in regulating nitrogen signaling and downstream genes within nitrogen metabolism [113].
Multiple highly conserved NLPs are found in Arabidopsis [112], wheat [114], and rice [112]. Arabidopsis NLPs function as transcription factors, and NLP7 also serves as a biosensor responsive to intracellular NO3− supply [115]. Rice NLPs generally serve as activators that control expression of nitrogen responsive genes. For example, NLP4 regulates expression of genes underlying key enzymes in nitrogen assimilation pathways [80], thereby affecting activities of NiR [81] and NR [116]. Some NLPs also shows nitrogen form-specific responses with NO3− being the major form to which rice NLPs are most responsive. While either NO3− or NH4+ can trigger expression of NLP3, only NO3− induces its nuclear retention [79]. Overexpression of these NLPs in rice stimulate yield, whereas reduced expression of NLPs inhibits growth. To date, yield improvements of wheat from modifications of NLPs are lacking, although nitrogen starvation upregulates NLP7 [114] and NLP4 [117].

3.4. Transcriptional Factors and microRNA That Regulate Other Genes (15 Genes)

Transcriptional factors bind to the promoter of target genes to regulate downstream gene functions [118]. System biology is steadily clarifying how a large network of transcription factors regulate nitrogen metabolism and how key transcription factors control expression of multiple proteins in the pathways simultaneously [119]. Whereas modifying expression of individual transporters and enzymes has had only modest success in improving crop performance, altering transcription factors that orchestrate simultaneously systematic changes in multiple nitrogen-related genes may have profound effects on biomass accumulation and grain quality. For example, overexpressing rice DREB1C, which regulates nitrogen assimilation genes, increased nitrogen assimilation and photosynthesis significantly, resulting in increased grain number, grain weight, and harvest index [95]. Together these changes resulted in 68.3% higher yield than wildtypes and in a 13 to 19 days earlier flowering time [95]. Light- and nitrogen-mediated OsDREB1C controlled over 9000 genome-wide putative binding sites, including five gene targets in the carbon and nitrogen metabolism pathways: rubisco small subunit 3 (OsRBCS3), OsNR2, nitrate transporter 2.4 (OsNRT2.4), OsNRT1.1B, and flowering locus T-like 1 (OsFTL1). Previous attempts to engineer several individual genes from this list never reached as high a yield gain as manipulating the transcriptional factor gene OsDREB1C alone. For instance, overexpression of transporters led to higher accumulation and efflux of excessive nitrogen because plants were not able to assimilate more nitrogen into protein [120].
Manually coordinating individual genes underlying nitrogen sources and sinks to complete a whole pathway therefore remains a challenge. Although we still have limited understanding about the regulation and function of transcription factors, modifying a single transcription factor appears more effective than manipulating individual genes and proteins in a pathway [121]. This highlights the complexity and tight regulation of nitrogen metabolism. As more genotypic and phenotypic data become available across diverse plant species, the roles of transcriptional factors in nitrogen metabolism should become clearer. Editing genetic networks, rather than individual candidate genes that regulate the balance between carbon and nitrogen metabolism may prove to be a more promising approach for increasing yield.

4. Prospects—Fine-Tuning Yield Component Responses to Transient Nitrogen Supply Can Maximize Yield

Successful manipulation of genes regulating nitrogen metabolism (Table 2) is contingent upon more advanced understanding of how nitrogen acquisition influences growth and vice versa. We now have a better understanding on how nitrogen, especially NO3−, drives hormonal and physiological changes underlying canopy architecture and development [122]. However, the influence nitrogen has on yield components and the tradeoffs among subcomponents are still uncertain. In this next section, we summarize recent findings, focusing on tiller number, flowering time, and NH4+ assimilation as key links between carbon and nitrogen metabolism and, therefore, highly relevant to nitrogen-driven yield improvement.

4.1. Tiller Production Contributes to Higher Yields through Multiple Nitrogen-Mediated Signaling Pathways

Tiller number, a key determent of effective number of panicles that contribute to grain filling and grain yield, is the most responsive of all yield components to nitrogen [122]. Tiller number is a routinely measured yield component because its assessment is straightforward. Out of the 40 genes reported to improve yield in this review (Table 2), 19 genes are associated with higher tiller number.
Increasing nitrogen supply generally increases tiller production [82] whereas limiting supply decreases tiller production [122]. Soil NH4+ concentration correlates linearly with tiller number in rice [123] and nitrogen fertilization levels explain 66% to 96% of the variation in tillering rate, which is significantly correlated with the final grain yield [23]. Similarly, increased nitrogen levels also boost tiller production in wheat [124]. Changing canopy architecture by optimizing nitrogen inputs and increasing tiller number per unit area thus enhances biomass source strength and grain yield in rice [125], and both yield and grain protein content in wheat [126].
Changes in tillering number derive from the interplay between multiple opposing nitrogen-mediated hormonal shifts [122]. High nitrogen availability induces cytokinins to increase tillering, but also induces auxins and strigolactones to inhibit tillering [127]. In rice, multiple amino acid transporters balance the opposite actions of auxins and cytokinins: OsAAP1 and OsAAP4 regulate auxin and cytokinin signaling [74,76], whereas OsAAP5 only influences cellular cytokinin levels [77]. microRNA393 (OsmiR393), in turn, lowers sensitivity to auxin signaling and increases tillering [128].
Feedback mechanisms between hormones and nitrogen ensure optimized developmental responses to fluctuating external nitrogen pools. As nitrogen supply increased, a negative feedback mechanism driven by DNR1 reduced auxin functions to upregulate genes for tiller production and nitrogen metabolism, thereby repressing nitrogen uptake and assimilation as well as tiller production [94]. Conversely, a nitrogen shortage downregulated DNR1, promoting nitrogen acquisition and tiller development [94].
The complex balancing acts of gibberellin, which explain why Green Revolution plant varieties maintain lower height and high yield, but require high nitrogen fertilizer inputs, have been reviewed in great detail elsewhere [106]. In brief, gibberellin and its counterparts DELLA proteins, which are named after their conserved chain of amino acids D-E-L-L-A, have two fates. Under high nitrogen availability, gibberellin can either inhibit tiller development by degrading gibberellin’s downstream transcriptional factor NGR5 protein or promote tiller production via a positive feedback mechanism driven by nitrogen itself to increase nitrogen assimilation and upregulate NGR5, which represses tiller inhibitory genes. Likewise, DELLAs may sustain tiller promotion by interfering with gibberellin-driven NGR5 destruction [82] or decrease nitrogen accumulation by downregulating nitrogen assimilation genes [42], thereby indirectly limiting nitrogen-driven tiller development. Because most Green Revolution-derived high-yielding cultivars already contain dwarfing genes conferring high DELLA abundance, breeders can further increase tiller production and yield even at low nitrogen levels by increasing NGR5 abundance directly, suggesting a potential decoupling of tillering from nitrogen supply [82].
Modification of transcriptional factors further enhances yield by tipping the balance of proteins and promoting nitrogen-driven tiller production. The coordination for carbon and nitrogen is systematically regulated by the transcription factor GRF4 [82] and its upstream repressor MIR396 [94], both of which modulate nitrogen acquisition and growth via DNR1 [94] and modulate nitrogen assimilation genes to counterbalance the inhibitory effects of DELLA [42]. Therefore, increased GRF4 expression alters the balance of GRF4-DELLA, thus enhancing nitrogen assimilation, tiller development, and grain yield [42].
Nitrogen influence on tiller development via the brassinosteroid signaling pathway also remains an active area of research. High NO3− levels decrease rice expression of TCP19, which represses Dwarf and Low-Tillering (DLT), a gene involved in brassinosteroid signaling and tillering promotion, thereby inhibiting tiller bud outgrowth [83]. OsTCP19 overexpression lines exhibit brassinosteroid-deficient phenotypes similar to dlt mutants [83]. In wheat, overexpression of Dwarf4 (DWF4), which encodes a key enzyme in brassinosteroid synthesis, also increases both nitrogen assimilation and tiller number [129]. Furthermore, the proteins of rice DLT and MONOCULM1 (MOC1), which regulate tiller production, are both under control of NGR5 [82].
Interestingly, there are tradeoffs among yield subcomponents. For example, not all yield improvement is associated with increased reproductive tiller number. In fact, fewer tillers is a key characteristic proposed as an ideal canopy architecture for high yields [130]. Mutants with loss-of-function dnr1 or reduced DNR1 abundance develop fewer tillers, but increase auxin, accelerate nitrogen uptake, and exhibit higher yields [94]. In the case of DREB1C overexpression, transgenic rice plants with higher yields have fewer panicle numbers, but instead produce elongated panicles with increased grain weight and number of grains within each panicle [95]. Such coordination between source and sink components appear to shift if carbohydrate supplies increase because these transgenic plants also have higher photosynthetic rates and accumulate more biomass at heading stage. Additionally, reduced branching may also result from a shortened development period to be discussed in the next section. Altogether, regulations of nitrogen-mediated tiller development highlight the importance of evaluating all yield components that contribute to actual yield changes.

4.2. Optimized Flowering Time Maximizes Nitrogen and Carbon Assimilation in Agricultural Settings

Adjustments of flowering time or heading date is an evolutionary adaptation that maximizes seed yield and survivability over generations [131]. Flowering time optimized for each environment can enhance grain yield in staple food crops [132]. The transition from vegetative to reproductive developmental stages determines total nitrogen accumulation over the vegetative growth period [25,32] and shifts the emphasis of nitrogen metabolism to remobilization and reassimilation in maturing grains [26]. While photosynthesis per unit leaf area may remain unchanged, cumulative increases in leaf area, light interception, overall growth period, and vegetative biomass accumulation—all responsive to nitrogen inputs—may together increase yields [133]. Suboptimal or excess nitrogen supply often, respectively, accelerate or slow the transition to reproductive phase [134]. The precise extent to which nitrogen supply influences cereal flowering time, however, is uncertain [135].
The genetics underlying vernalization and photoperiod pathways in cereals are well-characterized [136], but their interactions with nitrogen remain an open question. Multiple genes regulate flowering time and its influence on grain yield [132]. Indeed, genes underlying developmental timing like Photoperiod (Ppd) and Vernalization (Vrn) appear to co-locate with Quantitative Trait Loci (QTL) associated with NUE [137], suggesting a potential connection with nitrogen metabolism. Several recent studies have identified genes with pleiotropic effects that change both nitrogen responsiveness and crop developmental timing via senescence and flowering time. These include NPF6.3, GS2, Nhd1, Ghd7, ARE1, miR396 (Table 2). Specifically, transcription factors Nhd1, Ghd7 and DREB1C have direct control on genes involved in determining heading date [86,90,95]. Connections of other candidate genes with developmental timing require further validation.
An appropriate flower timing is essential for avoiding stressful conditions and maximizing favorable conditions for seed production [131]. Most genes in Table 2 promote a longer growing season. Prolonged vegetative growth generally allows crops to accumulate and assimilate more nitrogen before a crop reaches maturity and senescence, potentially resulting in increased NUE and biomass accumulation. However, a longer growth season may also increase the chance of experiencing abiotic and biotic stresses [131]. Only NPF6.3, DREB1C, and RDD1 accelerate a transition to the reproductive stage and still show a yield improvement [56,95,97]. For example, OsDREB1C significantly enhanced yield, despite a 2–3 weeks shorter growth period [95]. The ability to accumulate higher biomass under a shorter timeframe indicates a higher capacity for carbon and nitrogen assimilation. Nevertheless, early flowering time in rice with photo-insensitive alleles was previously shown to be associated with reduced grain filling, fewer panicles, and subsequently lower yield [138].
Developmental changes driven by variations in the growth environment determine the extent to which yields can be improved. Varying outcomes from different modifications may derive from the environmental interactions underlying nitrogen influence on growth and development. Late season tiller production may not produce a fertile fluorescence and thus contributes only to vegetative biomass production [35]. Tillers initiated early in the season also tend to have higher yields than late tillers [139]. High tiller production combined with longer maturation time generally contributes to higher rice yield [140]. For OsDREB1C modifications, overexpressing plants grown under long days and temperate climates flower about 50 to 70 days later and have higher yield improvement rates than plants under other experimental conditions [95]. Photoperiod pathways seem to be likely candidates that connect nitrogen responsiveness with flowering time, although no known mechanisms have been confirmed to date [134]. Understanding how carbon and nitrogen assimilation intersect and their environmental interaction in the context of crop developmental timing will be crucial in matching crop demands with resource supplies.

4.3. GOGAT as an Indirect Target—A Case Study from Editing Transcriptional Factors ARE1, Nhd1, and bZIP60

GOGAT, when coupled with GS, catalyze the assimilation of NH4+ into glutamate, an amino acid central to nitrogen and carbon metabolism [141]. Based on genetic map synteny, a meta-analysis of cereal QTL studies on NUE identified GOGAT as a candidate gene that is conserved among major food grain crops (rice, wheat, sorghum, maize) [137]. Although editing GOGAT directly has little influence on yield, coupling GOGAT with AMT1;2 proved effective in enhancing yield [69]. Modifying transcription factors upstream of GOGAT also has been successful (Table 2): ARE1 and Nhd1 both suppress Fd-GOGAT [87,89,90], while bZIP60 suppresses NADH-GOGAT [85].
Eliminating suppression of GOGAT via these transcription factors, improves yields significantly. Enhancement of GOGAT function seems to be a plausible approach for raising yield because glutamate links carbon and nitrogen metabolism [141]. The role of transcriptional factors suggests that we have yet to characterize additional players involved in the assimilation of NH4+ into organic nitrogen. Identification and modification of other pathways similar to GS/GOGAT -driven NH4+ assimilation, in that they influence both nitrogen acquisition and remobilization, may prove most effective for improving yields.

5. Puzzles—Knowledge Gaps about Modifying Nitrogen Metabolism for Yield Improvement

The current body of literature proffers open questions that require further investigation. In particular, studies that compare homeologs across species, crop responses to different inorganic nitrogen forms, and quantitative genetics underlying crop adaptation to natural soil nitrogen gradients should accelerate yield improvement through modified nitrogen metabolism.

5.1. Differences among Homologs across Species Remain Elusive

Comparative studies among species offer unique insights into finding related genes underlying desirable traits [142] such as for genes involved in C4 carbon fixation [95]. Nevertheless, transfer of successful breeding strategies across species remains challenging, even decades after fully characterizing most elements in nitrogen metabolism pathways [143].
To date, studies have identified more candidate genes and generated more breeding applications related to NUE in rice than in wheat [22,54] (Table 2). Translating insights from rice to wheat require herculean efforts, largely because of differences in genomic size and structure [22,144,145]. New mutant resources [146] and transgenic tools [147], however, increase the feasibility of characterizing candidate genes across a polyploid genome. Novel approaches like CRISPR-Cas9 system also further allows more precise editing of targeted loci of interest [148]. Even cross-species gene modifications such as transforming rice with wheat TaGS1 have proved successful in enhancing rice yield [149].
Multiple yield-determining genes are shared among rice, wheat, maize, and barley [22,150]. Identification of orthologous genes offers an alternative to introducing foreign genetic materials. Here, we discuss three examples: GOGAT, DREB1C and Ghd1. First, GOGAT is well-conserved in rice, wheat, sorghum and maize [137]. Editing transcription factors regulating GOGAT, however, seems more effective than modifying individual genes on their own (see discussion above). Second, rice OsDREB1C, whose overexpression increased yields up to 68.3%, has an ortholog in wheat TaDREB1C, whose overexpression results in 22.6% more grain yield than wildtypes [95]; the reason for the large differences in yield enhancement among species is not yet understood. Third, Ghd7 in rice and its ortholog VRN2 in wheat [86] have a high potential to improve agricultural performance. Both genes are well-studied and control flowering time in their respective species [151,152,153]. To date, however, there is no clear evidence on how VRN2 integrates signaling from nitrogen into regulation of flowering time in wheat. Given the promising yield enhancements attained with Ghd7 in rice, VRN2 might also provide major increases in wheat yields, but this is still unknown.

5.2. Insights on How Inorganic Nitrogen Forms Affect Crop Responses Are Lacking

Each form of inorganic nitrogen, NH4+ or NO3−, triggers specific crop responses [154]. In particular, an exposure to high concentration of soil NH4+ is generally toxic to most plants because root absorption of NH4+ may exceed the capacity of the plants to sequester the NH4+ in vacuoles or assimilate it into organic forms [103]. As free NH4+ accumulates within plant tissues, it can dissipate pH gradients through which mitochondrial and chloroplastic electron transport generate ATP [1]. To avoid such ill effects, plants generally assimilate NH4+ in roots and transport organic nitrogen compounds to other organs [103]. Optimizing root NH4+ accumulation and assimilation can enhance plant NH4+ tolerance and overall nitrogen acquisition [155]. By contrast, plants can store relatively large amounts of free NO3− without ill effect [1], and it serves as major signaling molecules for a number of metabolic pathways [156].
Although the importance of each inorganic form as a nitrogen source in crop production is well established [157], information is still meager on how each form induces or suppresses expression of nitrogen responsive genes or how changes in these genes in turn affect uptake and assimilation of each form. For example, NO3− transporter genes have a strong influence on NH4+ metabolism, and vice versa [158]. A more comprehensive understanding of these interactions would be crucial to designing and implementing more effective nitrogen fertilizer management strategies.
Relatively few studies compare responses to both form of inorganic nitrogen side by side, let alone evaluate the responses to a range of concentrations in diverse genetic materials. Although the model species Arabidopsis usually exhibited higher biomass and root production under NO3− nutrition, this species showed a wide range of distinct phenotypic responses and gene expression pattern when receiving NO3− or NH4+ as a sole nitrogen source [159]. Wheat growth under either form also demonstrated distinct accumulation and distribution patterns of other essential nutrients [160]. Nonetheless, we do not have sufficient information about the extent to which editing major genes in the nitrogen metabolism pathways changes responses to each inorganic form, and whether responses in wheat and rice are like those observed in Arabidopsis.
Most experiments to characterize genes reported in this review have only focused on a single nitrogen form or fail to designate the nitrogen form at all (Table 3). Detailed characterizations of individual nitrogen transporters may show that, not only are they responsible for uptake of both NO3− and NH4+ (for example, NRT 2.3b [64]), but also their functions have expanded and co-evolved to interact with other biotic and abiotic factors [161,162]. For example, NPF6.5 not only regulates NO3− uptake, but is also associated with recruitment of root microbes involved in the synthesis of NH4+ [158]. Understanding balance in crop utilization of both inorganic nitrogen forms will help us improve our crop and fertilization management in response to changing environments [163].
Nitrogen interacts more strongly with carbon assimilation as nitrogen supplies limit crop responses to enriched atmospheric CO2 levels [173]. Meta-analyses demonstrate that nutritional quality of wheat and rice—especially protein and micronutrients such as iron and zinc—decline significantly under elevated CO2 levels [174]. Among several alternative explanations for the declining crop protein at elevated CO2 levels [175,176,177,178], direct inhibition of shoot nitrogen assimilation [179] is most consistent with observations under a wide range of experimental conditions [180,181,182].
Photorespiration provides energy for shoot NO3− assimilation in C3 plants [3]. Photorespiration generates reductants when atmospheric CO2, but not light levels, limits photosynthesis and enables C3 plants to convert low energy nitrogen sources that most other organisms avoid like NO3− into organic nitrogen compounds. This confers an evolutionary advantage to C3 plants, which remain dominant among plant species [3]. Under the current rapid surge in atmospheric CO2 level, a condition which slows photorespiration, C3 species using NO3− as a nitrogen source suffer most from decreased organic nitrogen production [179,183]. N2-fixing legumes and C4 plants with CO2 concentrating mechanisms, are more resilient to changes in CO2 [174] because their inorganic nitrogen acquisition does not depend on photorespiration.
The use of NH4+-based nitrogen fertilizer and breeding for genotypes with improved NH4+ assimilation and tolerance may offer a solution for sustaining plant protein levels under future CO2-enriched atmospheres [184,185]. Biological Nitrification Inhibitors (BNI), which allow certain plant species to regulate their rhizosphere pools of inorganic nitrogen by releasing root exudates that specifically inhibit nitrifying bacteria that convert NH4+ into NO3−, may be beneficial [186]. Application of artificial BNI chemicals or incorporation of this trait into new cultivars may enhance crop growth under NH4+ nutrition [185,187].
Surprisingly, given the chemical differences between NO3− and NH4+, relatively little is known about how various nitrogen supplies shape crop adaptation and yield in a field setting (Table 3; see discussion below). The ability of the current germplasm to employ a specific nitrogen form as their predominant nitrogen source and maintain productivity at elevated CO2 levels thus remains an open question.

5.3. Little Information about Natural Genetic Variation and Genome-Wide Interactions Limits Breeding Applications

Despite an increasing understanding of physiological adaptation of roots and shoot to nitrogen supply [122], less is known about genetic adaptations [188]. Recent advances in genetic approaches greatly facilitate the identification of genes responsible for specific physiological traits. Of particular interest are Genome Wide Association Studies (GWAS) that use extensive sets of molecular markers to explore genetic variation resulting from historical recombinant events and from adaptation to changes in environmental conditions over evolutionary time [189]. Genetic architecture of traits also strongly influences GWAS robustness such that traits with rare alleles are more difficult to identify [190,191].
To date, a combination of GWAS and linkage mapping have identified many loci that underlie nitrogen responses of agricultural crops [192,193]. Importantly, GWAS enables deeper understanding of how environments may have shaped crop adaptation [194]. As such, natural variation of functional alleles can help inform breeding applications to achieve a better match between genotype and location [195]. Haplotype analyses in global germplasm quantifies allelic frequency of different breeding and natural subpopulations [196] and can offer practical strategies in breeding programs [197].
Selection pressures that vary during the history of crop domestication or with local limiting growth factors [198] provide insights into crop evolution and adaptation. More commonly, studies focus on differences between major subpopulations and how selection drives divergence or convergence between them. For example, divergence between the indica and japonica subpopulations of rice can be accounted by variations in key nitrogen metabolism genes like NPF6.5 [57] or NR2 [72]. Around 8% of the rice genome covering major nitrogen metabolism genes appear to be under selection including AMT1.1, NRT2.3, NAR2.2, NIR1, GS1;2, and GS1;3 [199]. Unfortunately, information is limited about the natural variation in candidate genes that enhance yield and the extent to which they have been under selection (Table 3). For instance, OsNPF6.1, which was identified through GWAS and functions under low nitrogen supply to increase NO3− uptake, is considered a rare allele, because it is present in less than 10% of cultivated varieties [55]. The absence or presence of a functional allele from diverse geographic regions may reflect adaptation to a particular soil nitrogen pool.
Apart from a few studies [83,86], we have limited information on the extent to which natural soil nitrogen availability shapes crop adaptation and, in turn, on subsequent responses to external nitrogen fertilizers in agricultural production systems. For example, Ghd7 allelic variation also correlates with soil nitrogen deposition rates [86]. Likewise, rice OsTCP19, which was identified through a GWAS on tiller responsiveness to nitrogen availability, has a functional allele frequency that is correlated with soil nitrogen concentration, and the nitrogen-responsive genotypes are more common in regions with low nitrogen concentrations [83]. Extensive networks of genes interact to sense and signal perception of nitrogen, especially NO3− [106]. Interestingly, expression of OsTCP19 follows changes in NO3−, but not NH4+ [83]. Overall, evidence is insufficient to conclude whether crops like wheat and rice, which have been exposed over the long term to certain nitrogen forms, show adaptation to a particular form. This information is vital for applying robust breeding strategies to improve future crops.
Genetic × environment interactions and expression patterns contingent upon growth conditions influence phenotypic plasticity [200,201], even when the same genes are being modified. Specifically, some genes may only be beneficial in certain environments or may even have detrimental pleiotropic effects in others. Field trials indicate that yield enhancement is highly dependent on growing conditions. For example, overexpression of ASN1 enhanced rice grain yield in pot experiments under limited nitrogen supply, but had no observable effect under sufficient nitrogen supply in the field [73]. In sites with a longer growing season, DREB1C transgenic plants exhibited a much higher yield boost compared to wildtypes [95]. With more advanced molecular breeding and transgenic approaches, promoters inducible in specific tissues or by desirable environmental triggers could perhaps mitigate such issues [12,121]. Precision genome editing methods, like the CRISPR-Cas9 system, facilitate genetic modifications at multiple target tissues, developmental times, and traits all at once without the introduction of foreign genetic materials [202]. Furthermore, advanced GWAS pipelines allow more explicit consideration of environmental variations to quantify plasticity and predict phenotype in a particular environment [203]. Better understanding of crop genetics, yield components, and their responses to the environment should bridge the gap between improved nitrogen metabolism and yield improvement.
Epistatic interactions further complicate breeding for candidate genes in different genetic backgrounds [204]. Gene or trait stacking based on our current understanding of each individual gene, protein, or process have had limited success to date, perhaps because of too little understanding of the complex regulatory network [205]. For example, the introduction of the grain protein content NAM-B1 transcription factor functional allele, which is generally absent from modern varieties, has only minimal influence on yield, but enhances grain nitrogen and protein content significantly across a wide range of environments [26,206,207]. A meta-analysis across 40 environments showed that 19% of bread wheat genotypes with NAM-B1 functional alleles exhibit yield enhancement [207], suggesting that the global germplasm still has genetic yield potential. Furthermore, combining multiple nitrogen metabolism genes in the pathway, for example NR2 and NPF6.5 [72], or AMT1;2 with GOGAT [69] offers greater chance of yield enhancement than modulating individual genes alone. These observations argue for manipulating either gene networks with multiple genes of relatively small effects or transcription factors that affect several genes and processes at the same time. Further understanding of system biology, especially underlying nitrogen metabolism, should prove useful in guiding such manipulations.

6. Concluding Remarks

Both yield and nitrogen metabolism pathways are complex traits with multiple layers of genetic control. While actual farm yield has increased in some regions of the world, increases in cereal potential yield—the scenario with no limitation on crop growth—have fallen down to below 1% annually [7]. We urgently need to apply new breeding strategies that accelerate genetic gains to meet the demands of our growing human population.
Here, we considered NUE on the basis of grain and total biomass production per unit of nitrogen applied or assimilated. Improvement of nitrogen acquisition, however, does not always translate into higher yields. For example, overexpression of transporter NPF7.4 resulted in higher NO3− uptake, lower NO3− accumulation, but higher tissue amino acid concentration, indicating improved nitrogen assimilation; nevertheless, such enhanced nitrogen acquisition decreased biomass and grain production [58]. Knocking out Lysine-Histidine-type Transporter 1 (LHT1), which transports amino acid, helped improve grain nutritional quality at maturity, but at the expense of vegetative biomass, grain weight, and germination rate [208,209]. Henceforth, defining and setting NUE as breeding targets to lower agricultural nitrogen inputs must take into account grain protein content [210,211]. These efforts are prime candidates for improving grain nutritional values. Therefore, if we define NUE as the amount of organic nitrogen that ends up in the consumable grains per nitrogen applied, these genes are worthy of consideration.
Breeding strategies that focus concurrently on both carbon and nitrogen assimilation also offer an opportunity to break the longstanding antagonistic relationship between grain biomass and protein concentration [212] that hampers genetic gains in yield over time. Genetic solutions are needed because management practices like applications of nitrogen fertilizers at booting stage to meet grain nitrogen demand can only partially alleviate this negative relationship at the field level [213]. Control of NGR5 that uncouples yield components from nitrogen-dependent responses [82], or GRF4 that breaks the tie between dwarfism-induced yield improvement and reduced nitrogen assimilation [42], establish the possibility of maximizing both yield and NUE at the same time. Genomic selection is theoretically feasible and genomic breeding tools are becoming readily available for breeders to target both sets of traits simultaneously [197,214].

7. Conclusions

This review highlights achievements in manipulating the genetics underlying nitrogen metabolism pathways to enhance yield of rice and wheat, focusing on relationships between yield components and crop nitrogen use during growth. Further fundamental understanding of ortholog genes between species, how different forms of nitrogen influence growth and development, and natural variation of desirable traits responsive to nitrogen should prove useful in achieving higher crop yields. Hopefully, continuous, albeit slow, progress on genetic gain in crop nitrogen assimilation and yield over time can fulfill the yield gap needed to feed our global community.

Author Contributions

Conceptualization, P.K. and A.J.B.; Methodology, P.K. and A.J.B.; Formal Analysis, P.K.; Investigation, P.K.; Data Curation, P.K.; Writing—Original Draft Preparation, P.K.; Writing—Review and Editing, P.K. and A.J.B.; Visualization, P.K.; Project Administration, A.J.B.; Funding Acquisition, P.K. and A.J.B. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported in part by USDA-IWYP-16-06702, NSF IOS 13-58675, NSF IOS 16-55810, and the John B. Orr Endowment to Arnold J. Bloom. Pornpipat Kasemsap was supported by Thailand—United States Educational Foundation (TUSEF/Fulbright Thailand), UC Davis Department of Plant Sciences Graduate Student Researcher fellowship, Henry A. Jastro research award, and Grad Innovator Fellowship from UC Davis Innovation Institute for Food and Health.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created in this study. Data sharing is not applicable to this article.

Acknowledgments

We thank Xiaoxiao Shi, Junli Zhang, and Carlos A. Robles-Zazueta for feedback on the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Bloom, A.J. Energetics of Nitrogen Acquisition. In Annual Plant Reviews Volume 42; Wiley-Blackwell: Hoboken, NJ, USA, 2010; pp. 63–81. ISBN 978-1-4443-2860-8. [Google Scholar]
  2. Ellis, R.J. The Most Abundant Protein in the World. Trends Biochem. Sci. 1979, 4, 241–244. [Google Scholar] [CrossRef]
  3. Bloom, A.J. Photorespiration and Nitrate Assimilation: A Major Intersection between Plant Carbon and Nitrogen. Photosynth. Res. 2015, 123, 117–128. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Xu, G.; Fan, X.; Miller, A.J. Plant Nitrogen Assimilation and Use Efficiency. Annu. Rev. Plant Biol. 2012, 63, 153–182. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Yang, C.J.; Ladejobi, O.; Mott, R.; Powell, W.; Mackay, I. Analysis of Historical Selection in Winter Wheat. Theor. Appl. Genet. 2022, 135, 3005–3023. [Google Scholar] [CrossRef] [PubMed]
  6. Chardon, F.; Noël, V.; Masclaux-Daubresse, C. Exploring NUE in Crops and in Arabidopsis Ideotypes to Improve Yield and Seed Quality. J. Exp. Bot. 2012, 63, 3401–3412. [Google Scholar] [CrossRef] [Green Version]
  7. Fischer, R.A.T.; Edmeades, G.O. Breeding and Cereal Yield Progress. Crop Sci. 2010, 50, S-85–S-98. [Google Scholar] [CrossRef] [Green Version]
  8. Ikeda, M.; Miura, K.; Aya, K.; Kitano, H.; Matsuoka, M. Genes Offering the Potential for Designing Yield-Related Traits in Rice. Curr. Opin. Plant Biol. 2013, 16, 213–220. [Google Scholar] [CrossRef]
  9. Reynolds, M.; Foulkes, J.; Furbank, R.; Griffiths, S.; King, J.; Murchie, E.; Parry, M.; Slafer, G. Achieving Yield Gains in Wheat: Achieving Yield Gains in Wheat. Plant Cell Environ. 2012, 35, 1799–1823. [Google Scholar] [CrossRef]
  10. Shearman, V.J.; Sylvester-Bradley, R.; Scott, R.K.; Foulkes, M.J. Physiological Processes Associated with Wheat Yield Progress in the UK. Crop Sci. 2005, 45, 175–185. [Google Scholar] [CrossRef]
  11. Sreenivasulu, N.; Schnurbusch, T. A Genetic Playground for Enhancing Grain Number in Cereals. Trends Plant Sci. 2012, 17, 91–101. [Google Scholar] [CrossRef]
  12. McAllister, C.H.; Beatty, P.H.; Good, A.G. Engineering Nitrogen Use Efficient Crop Plants: The Current Status: Engineering Nitrogen Use Efficient Crop Plants. Plant Biotechnol. J. 2012, 10, 1011–1025. [Google Scholar] [CrossRef]
  13. Tegeder, M.; Masclaux-Daubresse, C. Source and Sink Mechanisms of Nitrogen Transport and Use. New Phytol. 2018, 217, 35–53. [Google Scholar] [CrossRef] [Green Version]
  14. Britto, D.T.; Kronzucker, H.J. Ecological Significance and Complexity of N-Source Preference in Plants. Ann. Bot. 2013, 112, 957–963. [Google Scholar] [CrossRef] [Green Version]
  15. Kirk, G.J.D.; Kronzucker, H.J. The Potential for Nitrification and Nitrate Uptake in the Rhizosphere of Wetland Plants: A Modelling Study. Ann. Bot. 2005, 96, 639–646. [Google Scholar] [CrossRef] [Green Version]
  16. Jackson, L.E.; Schimel, J.P.; Firestone, M.K. Short-Term Partitioning of Ammonium and Nitrate between Plants and Microbes in an Annual Grassland. Soil Biol. Biochem. 1989, 21, 409–415. [Google Scholar] [CrossRef]
  17. Subbarao, G.V.; Sahrawat, K.L.; Nakahara, K.; Rao, I.M.; Ishitani, M.; Hash, C.T.; Kishii, M.; Bonnett, D.G.; Berry, W.L.; Lata, J.C. A Paradigm Shift towards Low-Nitrifying Production Systems: The Role of Biological Nitrification Inhibition (BNI). Ann. Bot. 2013, 112, 297–316. [Google Scholar] [CrossRef] [Green Version]
  18. Tischner, R. Nitrate Uptake and Reduction in Higher and Lower Plants. Plant Cell Environ. 2000, 23, 1005–1024. [Google Scholar] [CrossRef]
  19. FAO. FAOSTAT: Food Balances (2010-). Available online: https://www.fao.org/faostat/en/#data/FBS (accessed on 13 December 2022).
  20. Salse, J.; Bolot, S.; Throude, M.; Jouffe, V.; Piegu, B.; Quraishi, U.M.; Calcagno, T.; Cooke, R.; Delseny, M.; Feuillet, C. Identification and Characterization of Shared Duplications between Rice and Wheat Provide New Insight into Grass Genome Evolution. Plant Cell 2008, 20, 11–24. [Google Scholar] [CrossRef] [Green Version]
  21. Makino, A. Photosynthesis, Grain Yield, and Nitrogen Utilization in Rice and Wheat. Plant Physiol. 2011, 155, 125–129. [Google Scholar] [CrossRef] [Green Version]
  22. Valluru, R.; Reynolds, M.P.; Salse, J. Genetic and Molecular Bases of Yield-Associated Traits: A Translational Biology Approach between Rice and Wheat. Theor. Appl. Genet. 2014, 127, 1463–1489. [Google Scholar] [CrossRef]
  23. Fageria, N.K. Yield Physiology of Rice. J. Plant Nutr. 2007, 30, 843–879. [Google Scholar] [CrossRef]
  24. Sadras, V.O.; Slafer, G.A. Environmental Modulation of Yield Components in Cereals: Heritabilities Reveal a Hierarchy of Phenotypic Plasticities. Field Crop. Res. 2012, 127, 215–224. [Google Scholar] [CrossRef]
  25. Munier, D.; Kearney, T.; Pettygrove, G.S.; Brittan, K.; Mathews, M.; Jackson, L. Fertilization of Small Grains. In Small Grain Production Manual; UC ANR, Ed.; UC ANR: Davis, CA, USA, 2006. [Google Scholar]
  26. Uauy, C.; Distelfeld, A.; Fahima, T.; Blechl, A.; Dubcovsky, J. A NAC Gene Regulating Senescence Improves Grain Protein, Zinc, and Iron Content in Wheat. Science 2006, 314, 1298–1301. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Teng, W.; He, X.; Tong, Y. Genetic Control of Efficient Nitrogen Use for High Yield and Grain Protein Concentration in Wheat: A Review. Plants 2022, 11, 492. [Google Scholar] [CrossRef] [PubMed]
  28. Gaju, O.; Allard, V.; Martre, P.; Le Gouis, J.; Moreau, D.; Bogard, M.; Hubbart, S.; Foulkes, M.J. Nitrogen Partitioning and Remobilization in Relation to Leaf Senescence, Grain Yield and Grain Nitrogen Concentration in Wheat Cultivars. Field Crop. Res. 2014, 155, 213–223. [Google Scholar] [CrossRef]
  29. Zhang, Z.; Gao, S.; Chu, C. Improvement of Nutrient Use Efficiency in Rice: Current Toolbox and Future Perspectives. Theor. Appl. Genet. 2020, 133, 1365–1384. [Google Scholar] [CrossRef]
  30. Cormier, F.; Foulkes, J.; Hirel, B.; Gouache, D.; Moënne-Loccoz, Y.; Le Gouis, J. Breeding for Increased Nitrogen-Use Efficiency: A Review for Wheat (T. Aestivum L.). Plant Breed. 2016, 135, 255–278. [Google Scholar] [CrossRef] [Green Version]
  31. Good, A.G.; Shrawat, A.K.; Muench, D.G. Can Less Yield More? Is Reducing Nutrient Input into the Environment Compatible with Maintaining Crop Production? Trends Plant Sci. 2004, 9, 597–605. [Google Scholar] [CrossRef]
  32. Samonte, S.O.P.B.; Wilson, L.T.; Medley, J.C.; Pinson, S.R.M.; McClung, A.M.; Lales, J.S. Nitrogen Utilization Efficiency: Relationships with Grain Yield, Grain Protein, and Yield-Related Traits in Rice. Agron. J. 2006, 98, 168–176. [Google Scholar] [CrossRef] [Green Version]
  33. The, S.V.; Snyder, R.; Tegeder, M. Targeting Nitrogen Metabolism and Transport Processes to Improve Plant Nitrogen Use Efficiency. Front. Plant Sci. 2021, 11, 628366. [Google Scholar] [CrossRef]
  34. Sylvester-Bradley, R.; Kindred, D.R. Analysing Nitrogen Responses of Cereals to Prioritize Routes to the Improvement of Nitrogen Use Efficiency. J. Exp. Bot. 2009, 60, 1939–1951. [Google Scholar] [CrossRef] [Green Version]
  35. Sakamoto, T.; Matsuoka, M. Generating High-Yielding Varieties by Genetic Manipulation of Plant Architecture. Curr. Opin. Biotechnol. 2004, 15, 144–147. [Google Scholar] [CrossRef]
  36. Pearce, S. Towards the Replacement of Wheat ‘Green Revolution’ Genes. J. Exp. Bot. 2021, 72, 157–160. [Google Scholar] [CrossRef]
  37. Pearce, S.; Saville, R.; Vaughan, S.P.; Chandler, P.M.; Wilhelm, E.P.; Sparks, C.A.; Al-Kaff, N.; Korolev, A.; Boulton, M.I.; Phillips, A.L.; et al. Molecular Characterization of Rht-1 Dwarfing Genes in Hexaploid Wheat. Plant Physiol. 2011, 157, 1820–1831. [Google Scholar] [CrossRef] [Green Version]
  38. Peng, J.; Richards, D.E.; Hartley, N.M.; Murphy, G.P.; Devos, K.M.; Flintham, J.E.; Beales, J.; Fish, L.J.; Worland, A.J.; Pelica, F.; et al. ‘Green Revolution’ Genes Encode Mutant Gibberellin Response Modulators. Nature 1999, 400, 256–261. [Google Scholar] [CrossRef]
  39. Sasaki, A.; Ashikari, M.; Ueguchi-Tanaka, M.; Itoh, H.; Nishimura, A.; Swapan, D.; Ishiyama, K.; Saito, T.; Kobayashi, M.; Khush, G.S.; et al. A Mutant Gibberellin-Synthesis Gene in Rice. Nature 2002, 416, 701–702. [Google Scholar] [CrossRef]
  40. Sinclair, T.R. Historical Changes in Harvest Index and Crop Nitrogen Accumulation. Crop Sci. 1998, 38, 638–643. [Google Scholar] [CrossRef]
  41. Gooding, M.J.; Addisu, M.; Uppal, R.K.; Snape, J.W.; Jones, H.E. Effect of Wheat Dwarfing Genes on Nitrogen-Use Efficiency. J. Agric. Sci. 2012, 150, 3–22. [Google Scholar] [CrossRef] [Green Version]
  42. Li, S.; Tian, Y.; Wu, K.; Ye, Y.; Yu, J.; Zhang, J.; Liu, Q.; Hu, M.; Li, H.; Tong, Y.; et al. Modulating Plant Growth–Metabolism Coordination for Sustainable Agriculture. Nature 2018, 560, 595–600. [Google Scholar] [CrossRef]
  43. Kelly, S. The Quest for More Food. Science 2022, 377, 370–371. [Google Scholar] [CrossRef]
  44. Reynolds, M.P.; Slafer, G.A.; Foulkes, J.M.; Griffiths, S.; Murchie, E.H.; Carmo-Silva, E.; Asseng, S.; Chapman, S.C.; Sawkins, M.; Gwyn, J.; et al. A Wiring Diagram to Integrate Physiological Traits of Wheat Yield Potential. Nat. Food 2022, 3, 318–324. [Google Scholar] [CrossRef]
  45. Long, S.P.; Zhu, X.-G.; Naidu, S.L.; Ort, D.R. Can Improvement in Photosynthesis Increase Crop Yields? Plant Cell Environ. 2006, 29, 315–330. [Google Scholar] [CrossRef] [PubMed]
  46. Sinclair, T.R.; Rufty, T.W.; Lewis, R.S. Increasing Photosynthesis: Unlikely Solution For World Food Problem. Trends Plant Sci. 2019, 24, 1032–1039. [Google Scholar] [CrossRef] [PubMed]
  47. Yoon, D.-K.; Ishiyama, K.; Suganami, M.; Tazoe, Y.; Watanabe, M.; Imaruoka, S.; Ogura, M.; Ishida, H.; Suzuki, Y.; Obara, M.; et al. Transgenic Rice Overproducing Rubisco Exhibits Increased Yields with Improved Nitrogen-Use Efficiency in an Experimental Paddy Field. Nat. Food 2020, 1, 134–139. [Google Scholar] [CrossRef] [Green Version]
  48. Araus, J.L.; Sanchez-Bragado, R.; Vicente, R. Improving Crop Yield and Resilience through Optimization of Photosynthesis: Panacea or Pipe Dream? J. Exp. Bot. 2021, 72, 3936–3955. [Google Scholar] [CrossRef]
  49. Lee, S. Recent Advances on Nitrogen Use Efficiency in Rice. Agronomy 2021, 11, 753. [Google Scholar] [CrossRef]
  50. Prelich, G. Gene Overexpression: Uses, Mechanisms, and Interpretation. Genetics 2012, 190, 841–854. [Google Scholar] [CrossRef] [Green Version]
  51. Monroe, J.G.; Arciniegas, J.P.; Moreno, J.L.; Sánchez, F.; Sierra, S.; Valdes, S.; Torkamaneh, D.; Chavarriaga, P. The Lowest Hanging Fruit: Beneficial Gene Knockouts in Past, Present, and Future Crop Evolution. Curr. Plant Biol. 2020, 24, 100185. [Google Scholar] [CrossRef]
  52. Ossowski, S.; Schwab, R.; Weigel, D. Gene Silencing in Plants Using Artificial MicroRNAs and Other Small RNAs: Engineering Small RNA-Mediated Gene Silencing. Plant J. 2008, 53, 674–690. [Google Scholar] [CrossRef]
  53. Chen, K.; Wang, Y.; Zhang, R.; Zhang, H.; Gao, C. CRISPR/Cas Genome Editing and Precision Plant Breeding in Agriculture. Annu. Rev. Plant Biol. 2019, 70, 667–697. [Google Scholar] [CrossRef]
  54. Fan, X.; Naz, M.; Fan, X.; Xuan, W.; Miller, A.J.; Xu, G. Plant Nitrate Transporters: From Gene Function to Application. J. Exp. Bot. 2017, 68, 2463–2475. [Google Scholar] [CrossRef]
  55. Tang, W.; Ye, J.; Yao, X.; Zhao, P.; Xuan, W.; Tian, Y.; Zhang, Y.; Xu, S.; An, H.; Chen, G.; et al. Genome-Wide Associated Study Identifies NAC42-Activated Nitrate Transporter Conferring High Nitrogen Use Efficiency in Rice. Nat. Commun. 2019, 10, 5279. [Google Scholar] [CrossRef] [Green Version]
  56. Wang, W.; Hu, B.; Yuan, D.; Liu, Y.; Che, R.; Hu, Y.; Ou, S.; Liu, Y.; Zhang, Z.; Wang, H.; et al. Expression of the Nitrate Transporter Gene OsNRT1.1A/OsNPF6.3 Confers High Yield and Early Maturation in Rice. Plant Cell 2018, 30, 638–651. [Google Scholar] [CrossRef] [Green Version]
  57. Hu, B.; Wang, W.; Ou, S.; Tang, J.; Li, H.; Che, R.; Zhang, Z.; Chai, X.; Wang, H.; Wang, Y.; et al. Variation in NRT1.1B Contributes to Nitrate-Use Divergence between Rice Subspecies. Nat. Genet. 2015, 47, 834–838. [Google Scholar] [CrossRef]
  58. Huang, W.; Nie, H.; Feng, F.; Wang, J.; Lu, K.; Fang, Z. Altered Expression of OsNPF7.1 and OsNPF7.4 Differentially Regulates Tillering and Grain Yield in Rice. Plant Sci. 2019, 283, 23–31. [Google Scholar] [CrossRef]
  59. Wang, J.; Lu, K.; Nie, H.; Zeng, Q.; Wu, B.; Qian, J.; Fang, Z. Rice Nitrate Transporter OsNPF7.2 Positively Regulates Tiller Number and Grain Yield. Rice 2018, 11, 12. [Google Scholar] [CrossRef] [Green Version]
  60. Huang, W.; Bai, G.; Wang, J.; Zhu, W.; Zeng, Q.; Lu, K.; Sun, S.; Fang, Z. Two Splicing Variants of OsNPF7.7 Regulate Shoot Branching and Nitrogen Utilization Efficiency in Rice. Front. Plant Sci. 2018, 9, 300. [Google Scholar] [CrossRef] [Green Version]
  61. Fang, Z.; Xia, K.; Yang, X.; Grotemeyer, M.S.; Meier, S.; Rentsch, D.; Xu, X.; Zhang, M. Altered Expression of the PTR/NRT1 Homologue OsPTR9 Affects Nitrogen Utilization Efficiency, Growth and Grain Yield in Rice. Plant Biotechnol. J. 2013, 11, 446–458. [Google Scholar] [CrossRef]
  62. Luo, B.; Chen, J.; Zhu, L.; Liu, S.; Li, B.; Lu, H.; Ye, G.; Xu, G.; Fan, X. Overexpression of a High-Affinity Nitrate Transporter OsNRT2.1 Increases Yield and Manganese Accumulation in Rice Under Alternating Wet and Dry Condition. Front. Plant Sci. 2018, 9, 1192. [Google Scholar] [CrossRef] [Green Version]
  63. Yan, M.; Fan, X.; Feng, H.; Miller, A.J.; Shen, Q.; Xu, G. Rice OsNAR2.1 Interacts with OsNRT2.1, OsNRT2.2 and OsNRT2.3a Nitrate Transporters to Provide Uptake over High and Low Concentration Ranges: Rice Two Component Nitrate Transport. Plant Cell Environ. 2011, 34, 1360–1372. [Google Scholar] [CrossRef]
  64. Fan, X.; Tang, Z.; Tan, Y.; Zhang, Y.; Luo, B.; Yang, M.; Lian, X.; Shen, Q.; Miller, A.J.; Xu, G. Overexpression of a PH-Sensitive Nitrate Transporter in Rice Increases Crop Yields. Proc. Natl. Acad. Sci. USA 2016, 113, 7118–7123. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Chen, J.; Liu, X.; Liu, S.; Fan, X.; Zhao, L.; Song, M.; Fan, X.; Xu, G. Co-Overexpression of OsNAR2.1 and OsNRT2.3a Increased Agronomic Nitrogen Use Efficiency in Transgenic Rice Plants. Front. Plant Sci. 2020, 11, 1245. [Google Scholar] [CrossRef] [PubMed]
  66. Chen, J.; Qi, T.; Hu, Z.; Fan, X.; Zhu, L.; Iqbal, M.F.; Yin, X.; Xu, G.; Fan, X. OsNAR2.1 Positively Regulates Drought Tolerance and Grain Yield Under Drought Stress Conditions in Rice. Front. Plant Sci. 2019, 10, 197. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  67. Chen, J.; Fan, X.; Qian, K.; Zhang, Y.; Song, M.; Liu, Y.; Xu, G.; Fan, X. POsNAR2.1:OsNAR2.1 Expression Enhances Nitrogen Uptake Efficiency and Grain Yield in Transgenic Rice Plants. Plant Biotechnol. J. 2017, 15, 1273–1283. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  68. Ranathunge, K.; El-kereamy, A.; Gidda, S.; Bi, Y.-M.; Rothstein, S.J. AMT1;1 Transgenic Rice Plants with Enhanced NH4+ Permeability Show Superior Growth and Higher Yield under Optimal and Suboptimal NH4+ Conditions. J. Exp. Bot. 2014, 65, 965–979. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  69. Lee, S.; Marmagne, A.; Park, J.; Fabien, C.; Yim, Y.; Kim, S.; Kim, T.; Lim, P.O.; Masclaux-Daubresse, C.; Nam, H.G. Concurrent Activation of OsAMT1;2 and OsGOGAT1 in Rice Leads to Enhanced Nitrogen Use Efficiency under Nitrogen Limitation. Plant J. 2020, 103, 7–20. [Google Scholar] [CrossRef]
  70. James, D.; Borphukan, B.; Fartyal, D.; Ram, B.; Singh, J.; Manna, M.; Sheri, V.; Panditi, V.; Yadav, R.; Achary, V.M.M.; et al. Concurrent Overexpression of OsGS1;1 and OsGS2 Genes in Transgenic Rice (Oryza Sativa L.): Impact on Tolerance to Abiotic Stresses. Front. Plant Sci. 2018, 9, 786. [Google Scholar] [CrossRef]
  71. Hu, M.; Zhao, X.; Liu, Q.; Hong, X.; Zhang, W.; Zhang, Y.; Sun, L.; Li, H.; Tong, Y. Transgenic Expression of Plastidic Glutamine Synthetase Increases Nitrogen Uptake and Yield in Wheat. Plant Biotechnol. J. 2018, 16, 1858–1867. [Google Scholar] [CrossRef]
  72. Gao, Z.; Wang, Y.; Chen, G.; Zhang, A.; Yang, S.; Shang, L.; Wang, D.; Ruan, B.; Liu, C.; Jiang, H.; et al. The Indica Nitrate Reductase Gene OsNR2 Allele Enhances Rice Yield Potential and Nitrogen Use Efficiency. Nat. Commun. 2019, 10, 5207. [Google Scholar] [CrossRef] [Green Version]
  73. Lee, S.; Park, J.; Lee, J.; Shin, D.; Marmagne, A.; Lim, P.O.; Masclaux-Daubresse, C.; An, G.; Nam, H.G. OsASN1 Overexpression in Rice Increases Grain Protein Content and Yield under Nitrogen-Limiting Conditions. Plant Cell Physiol. 2020, 61, 1309–1320. [Google Scholar] [CrossRef]
  74. Ji, Y.; Huang, W.; Wu, B.; Fang, Z.; Wang, X. The Amino Acid Transporter AAP1 Mediates Growth and Grain Yield by Regulating Neutral Amino Acid Uptake and Reallocation in Oryza Sativa. J. Exp. Bot. 2020, 71, 4763–4777. [Google Scholar] [CrossRef]
  75. Lu, K.; Wu, B.; Wang, J.; Zhu, W.; Nie, H.; Qian, J.; Huang, W.; Fang, Z. Blocking Amino Acid Transporter Os AAP 3 Improves Grain Yield by Promoting Outgrowth Buds and Increasing Tiller Number in Rice. Plant Biotechnol. J. 2018, 16, 1710–1722. [Google Scholar] [CrossRef] [Green Version]
  76. Fang, Z.; Wu, B.; Ji, Y. The Amino Acid Transporter OsAAP4 Contributes to Rice Tillering and Grain Yield by Regulating Neutral Amino Acid Allocation through Two Splicing Variants. Rice 2021, 14, 2. [Google Scholar] [CrossRef]
  77. Wang, J.; Wu, B.; Lu, K.; Wei, Q.; Qian, J.; Chen, Y.; Fang, Z. The Amino Acid Permease 5 (OsAAP5) Regulates Tiller Number and Grain Yield in Rice. Plant Physiol. 2019, 180, 1031–1045. [Google Scholar] [CrossRef]
  78. Alfatih, A.; Wu, J.; Zhang, Z.-S.; Xia, J.-Q.; Jan, S.U.; Yu, L.-H.; Xiang, C.-B. Rice NIN-LIKE PROTEIN 1 Rapidly Responds to Nitrogen Deficiency and Improves Yield and Nitrogen Use Efficiency. J. Exp. Bot. 2020, 71, 6032–6042. [Google Scholar] [CrossRef]
  79. Zhang, Z.; Xia, J.; Alfatih, A.; Song, Y.; Huang, Y.; Sun, L.; Wan, G.; Wang, S.; Wang, Y.; Hu, B.; et al. Rice NIN-LIKE PROTEIN 3 Modulates Nitrogen Use Efficiency and Grain Yield under Nitrate-sufficient Conditions. Plant Cell Environ. 2022, 45, 1520–1536. [Google Scholar] [CrossRef]
  80. Wu, J.; Zhang, Z.; Xia, J.; Alfatih, A.; Song, Y.; Huang, Y.; Wan, G.; Sun, L.; Tang, H.; Liu, Y.; et al. Rice NIN-LIKE PROTEIN 4 Plays a Pivotal Role in Nitrogen Use Efficiency. Plant Biotechnol. J. 2021, 19, 448–461. [Google Scholar] [CrossRef]
  81. Yu, J.; Xuan, W.; Tian, Y.; Fan, L.; Sun, J.; Tang, W.; Chen, G.; Wang, B.; Liu, Y.; Wu, W.; et al. Enhanced OsNLP4-OsNiR Cascade Confers Nitrogen Use Efficiency by Promoting Tiller Number in Rice. Plant Biotechnol. J. 2021, 19, 167–176. [Google Scholar] [CrossRef]
  82. Wu, K.; Wang, S.; Song, W.; Zhang, J.; Wang, Y.; Liu, Q.; Yu, J.; Ye, Y.; Li, S.; Chen, J.; et al. Enhanced Sustainable Green Revolution Yield via Nitrogen-Responsive Chromatin Modulation in Rice. Science 2020, 367, eaaz2046. [Google Scholar] [CrossRef]
  83. Liu, Y.; Wang, H.; Jiang, Z.; Wang, W.; Xu, R.; Wang, Q.; Zhang, Z.; Li, A.; Liang, Y.; Ou, S.; et al. Genomic Basis of Geographical Adaptation to Soil Nitrogen in Rice. Nature 2021, 590, 600–605. [Google Scholar] [CrossRef]
  84. He, X.; Qu, B.; Li, W.; Zhao, X.; Teng, W.; Ma, W.; Ren, Y.; Li, B.; Li, Z.; Tong, Y. The Nitrate Inducible NAC Transcription Factor TaNAC2-5A Controls Nitrate Response and Increases Wheat Yield. Plant Physiol. 2015, 169, 1991–2005. [Google Scholar] [CrossRef] [Green Version]
  85. Yang, J.; Wang, M.; Li, W.; He, X.; Teng, W.; Ma, W.; Zhao, X.; Hu, M.; Li, H.; Zhang, Y.; et al. Reducing Expression of a Nitrate-responsive bZIP Transcription Factor Increases Grain Yield and N Use in Wheat. Plant Biotechnol. J. 2019, 17, 1823–1833. [Google Scholar] [CrossRef]
  86. Wang, Q.; Su, Q.; Nian, J.; Zhang, J.; Guo, M.; Dong, G.; Hu, J.; Wang, R.; Wei, C.; Li, G.; et al. The Ghd7 Transcription Factor Represses ARE1 Expression to Enhance Nitrogen Utilization and Grain Yield in Rice. Mol. Plant 2021, 14, 1012–1023. [Google Scholar] [CrossRef] [PubMed]
  87. Wang, Q.; Nian, J.; Xie, X.; Yu, H.; Zhang, J.; Bai, J.; Dong, G.; Hu, J.; Bai, B.; Chen, L.; et al. Genetic Variations in ARE1 Mediate Grain Yield by Modulating Nitrogen Utilization in Rice. Nat. Commun. 2018, 9, 735. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  88. Zhang, J.; Zhang, H.; Li, S.; Li, J.; Yan, L.; Xia, L. Increasing Yield Potential through Manipulating of an ARE1 Ortholog Related to Nitrogen Use Efficiency in Wheat by CRISPR/Cas9. J. Integr. Plant Biol. 2021, 63, 1649–1663. [Google Scholar] [CrossRef] [PubMed]
  89. Zhang, S.; Zhang, Y.; Li, K.; Yan, M.; Zhang, J.; Yu, M.; Tang, S.; Wang, L.; Qu, H.; Luo, L.; et al. Nitrogen Mediates Flowering Time and Nitrogen Use Efficiency via Floral Regulators in Rice. Curr. Biol. 2021, 31, 671–683.e5. [Google Scholar] [CrossRef]
  90. Li, K.; Zhang, S.; Tang, S.; Zhang, J.; Dong, H.; Yang, S.; Qu, H.; Xuan, W.; Gu, M.; Xu, G. The Rice Transcription Factor Nhd1 Regulates Root Growth and Nitrogen Uptake by Activating Nitrogen Transporters. Plant Physiol. 2022, 189, 1608–1624. [Google Scholar] [CrossRef]
  91. Sun, H.; Qian, Q.; Wu, K.; Luo, J.; Wang, S.; Zhang, C.; Ma, Y.; Liu, Q.; Huang, X.; Yuan, Q.; et al. Heterotrimeric G Proteins Regulate Nitrogen-Use Efficiency in Rice. Nat. Genet. 2014, 46, 652–656. [Google Scholar] [CrossRef]
  92. Huang, X.; Qian, Q.; Liu, Z.; Sun, H.; He, S.; Luo, D.; Xia, G.; Chu, C.; Li, J.; Fu, X. Natural Variation at the DEP1 Locus Enhances Grain Yield in Rice. Nat. Genet. 2009, 41, 494–497. [Google Scholar] [CrossRef]
  93. Zhao, M.; Zhao, M.; Gu, S.; Sun, J.; Ma, Z.; Wang, L.; Zheng, W.; Xu, Z. DEP1 Is Involved in Regulating the Carbon–Nitrogen Metabolic Balance to Affect Grain Yield and Quality in Rice (Oriza Sativa L.). PLoS ONE 2019, 14, e0213504. [Google Scholar] [CrossRef]
  94. Zhang, S.; Zhu, L.; Shen, C.; Ji, Z.; Zhang, H.; Zhang, T.; Li, Y.; Yu, J.; Yang, N.; He, Y.; et al. Natural Allelic Variation in a Modulator of Auxin Homeostasis Improves Grain Yield and Nitrogen Use Efficiency in Rice. Plant Cell 2021, 33, 566–580. [Google Scholar] [CrossRef]
  95. Wei, S.; Li, X.; Lu, Z.; Zhang, H.; Ye, X.; Zhou, Y.; Li, J.; Yan, Y.; Pei, H.; Duan, F.; et al. A Transcriptional Regulator That Boosts Grain Yields and Shortens the Growth Duration of Rice. Science 2022, 377, eabi8455. [Google Scholar] [CrossRef]
  96. Qu, B.; He, X.; Wang, J.; Zhao, Y.; Teng, W.; Shao, A.; Zhao, X.; Ma, W.; Wang, J.; Li, B.; et al. A Wheat CCAAT Box-Binding Transcription Factor Increases the Grain Yield of Wheat with Less Fertilizer Input. Plant Physiol. 2015, 167, 411–423. [Google Scholar] [CrossRef] [Green Version]
  97. Iwamoto, M.; Tagiri, A. Micro RNA -targeted Transcription Factor Gene RDD 1 Promotes Nutrient Ion Uptake and Accumulation in Rice. Plant J. 2016, 85, 466–477. [Google Scholar] [CrossRef] [Green Version]
  98. Zhang, J.; Zhou, Z.; Bai, J.; Tao, X.; Wang, L.; Zhang, H.; Zhu, J.-K. Disruption of MIR396e and MIR396f Improves Rice Yield under Nitrogen-Deficient Conditions. Natl. Sci. Rev. 2020, 7, 102–112. [Google Scholar] [CrossRef] [Green Version]
  99. Von Wittgenstein, N.J.; Le, C.H.; Hawkins, B.J.; Ehlting, J. Evolutionary Classification of Ammonium, Nitrate, and Peptide Transporters in Land Plants. BMC Evol. Biol. 2014, 14, 11. [Google Scholar] [CrossRef] [Green Version]
  100. Li, H.; Hu, B.; Chu, C. Nitrogen Use Efficiency in Crops: Lessons from Arabidopsis and Rice. J. Exp. Bot. 2017, 68, 2477–2488. [Google Scholar] [CrossRef] [Green Version]
  101. Buchner, P.; Hawkesford, M.J. Complex Phylogeny and Gene Expression Patterns of Members of the NITRATE TRANSPORTER 1/PEPTIDE TRANSPORTER Family (NPF) in Wheat. J. Exp. Bot. 2014, 65, 5697–5710. [Google Scholar] [CrossRef] [Green Version]
  102. Kumar, A.; Sandhu, N.; Kumar, P.; Pruthi, G.; Singh, J.; Kaur, S.; Chhuneja, P. Genome-Wide Identification and in Silico Analysis of NPF, NRT2, CLC and SLAC1/SLAH Nitrate Transporters in Hexaploid Wheat (Triticum aestivum). Sci. Rep. 2022, 12, 11227. [Google Scholar] [CrossRef]
  103. Britto, D.T.; Kronzucker, H.J. NH4+ Toxicity in Higher Plants: A Critical Review. J. Plant Physiol. 2002, 159, 567–584. [Google Scholar] [CrossRef]
  104. Guo, N.; Zhang, S.; Gu, M.; Xu, G. Function, Transport, and Regulation of Amino Acids: What Is Missing in Rice? Crop J. 2021, 9, 530–542. [Google Scholar] [CrossRef]
  105. Plett, D.C.; Holtham, L.R.; Okamoto, M.; Garnett, T.P. Nitrate Uptake and Its Regulation in Relation to Improving Nitrogen Use Efficiency in Cereals. Semin. Cell Dev. Biol. 2018, 74, 97–104. [Google Scholar] [CrossRef] [PubMed]
  106. Liu, Q.; Wu, K.; Song, W.; Zhong, N.; Wu, Y.; Fu, X. Improving Crop Nitrogen Use Efficiency Toward Sustainable Green Revolution. Annu. Rev. Plant Biol. 2022, 73, 523–551. [Google Scholar] [CrossRef] [PubMed]
  107. Brauer, E.K.; Rochon, A.; Bi, Y.-M.; Bozzo, G.G.; Rothstein, S.J.; Shelp, B.J. Reappraisal of Nitrogen Use Efficiency in Rice Overexpressing Glutamine Synthetase1. Physiol. Plant. 2011, 141, 361–372. [Google Scholar] [CrossRef] [PubMed]
  108. Thomsen, H.C.; Eriksson, D.; Møller, I.S.; Schjoerring, J.K. Cytosolic Glutamine Synthetase: A Target for Improvement of Crop Nitrogen Use Efficiency? Trends Plant Sci. 2014, 19, 656–663. [Google Scholar] [CrossRef]
  109. Zhang, Z.; Xiong, S.; Wei, Y.; Meng, X.; Wang, X.; Ma, X. The Role of Glutamine Synthetase Isozymes in Enhancing Nitrogen Use Efficiency of N-Efficient Winter Wheat. Sci. Rep. 2017, 7, 1000. [Google Scholar] [CrossRef] [Green Version]
  110. Wei, Y.; Xiong, S.; Zhang, Z.; Meng, X.; Wang, L.; Zhang, X.; Yu, M.; Yu, H.; Wang, X.; Ma, X. Localization, Gene Expression, and Functions of Glutamine Synthetase Isozymes in Wheat Grain (Triticum aestivum L.). Front. Plant Sci. 2021, 12, 580405. [Google Scholar] [CrossRef]
  111. Schulte, C.C.M.; Borah, K.; Wheatley, R.M.; Terpolilli, J.J.; Saalbach, G.; Crang, N.; de Groot, D.H.; Ratcliffe, R.G.; Kruger, N.J.; Papachristodoulou, A.; et al. Metabolic Control of Nitrogen Fixation in Rhizobium-Legume Symbioses. Sci. Adv. 2021, 7, eabh2433. [Google Scholar] [CrossRef]
  112. Schauser, L.; Wieloch, W.; Stougaard, J. Evolution of NIN-Like Proteins in Arabidopsis, Rice, and Lotus Japonicus. J. Mol. Evol. 2005, 60, 229–237. [Google Scholar] [CrossRef]
  113. Mu, X.; Luo, J. Evolutionary Analyses of NIN-like Proteins in Plants and Their Roles in Nitrate Signaling. Cell. Mol. Life Sci. 2019, 76, 3753–3764. [Google Scholar] [CrossRef]
  114. Kumar, A.; Batra, R.; Gahlaut, V.; Gautam, T.; Kumar, S.; Sharma, M.; Tyagi, S.; Singh, K.P.; Balyan, H.S.; Pandey, R.; et al. Genome-Wide Identification and Characterization of Gene Family for RWP-RK Transcription Factors in Wheat (Triticum aestivum L.). PLoS ONE 2018, 13, e0208409. [Google Scholar] [CrossRef] [Green Version]
  115. Liu, K.-H.; Liu, M.; Lin, Z.; Wang, Z.-F.; Chen, B.; Liu, C.; Guo, A.; Konishi, M.; Yanagisawa, S.; Wagner, G.; et al. NIN-like Protein 7 Transcription Factor Is a Plant Nitrate Sensor. Science 2022, 377, 1419–1425. [Google Scholar] [CrossRef]
  116. Wang, M.; Hasegawa, T.; Beier, M.; Hayashi, M.; Ohmori, Y.; Yano, K.; Teramoto, S.; Kamiya, T.; Fujiwara, T. Growth and Nitrate Reductase Activity Are Impaired in Rice Osnlp4 Mutants Supplied with Nitrate. Plant Cell Physiol. 2021, 62, 1156–1167. [Google Scholar] [CrossRef]
  117. Mahmoud, D.; Pandey, R.; Sathee, L.; Dalal, M.; Singh, M.P.; Chinnusamy, V. Regulation of Expression of Genes Associated with Nitrate Response by Osmotic Stress and Combined Osmotic and Nitrogen Deficiency Stress in Bread Wheat (Triticum aestivum L.). Plant Physiol. Rep. 2020, 25, 200–215. [Google Scholar] [CrossRef]
  118. Latchman, D.S. Transcription Factors: An Overview. Int. J. Biochem. Cell Biol. 1997, 29, 1305–1312. [Google Scholar] [CrossRef] [Green Version]
  119. Gaudinier, A.; Rodriguez-Medina, J.; Zhang, L.; Olson, A.; Liseron-Monfils, C.; Bågman, A.-M.; Foret, J.; Abbitt, S.; Tang, M.; Li, B.; et al. Transcriptional Regulation of Nitrogen-Associated Metabolism and Growth. Nature 2018, 563, 259–264. [Google Scholar] [CrossRef] [Green Version]
  120. Britto, D.T.; Kronzucker, H.J. Bioengineering Nitrogen Acquisition in Rice: Can Novel Initiatives in Rice Genomics and Physiology Contribute to Global Food Security? BioEssays 2004, 26, 683–692. [Google Scholar] [CrossRef]
  121. Ueda, Y.; Yanagisawa, S. Transcription Factor-Based Genetic Engineering to Increase Nitrogen Use Efficiency. In Engineering Nitrogen Utilization in Crop Plants; Shrawat, A., Zayed, A., Lightfoot, D.A., Eds.; Springer International Publishing: Cham, Switzerland, 2018; pp. 37–55. ISBN 978-3-319-92957-6. [Google Scholar]
  122. Luo, L.; Zhang, Y.; Xu, G. How Does Nitrogen Shape Plant Architecture? J. Exp. Bot. 2020, 71, 4415–4427. [Google Scholar] [CrossRef]
  123. Sasaki, Y.; Ando, H.; Kakuda, K. Relationship between Ammonium Nitrogen in Soil Solution and Tiller Number at Early Growth Stage of Rice. Soil Sci. Plant Nutr. 2002, 48, 57–63. [Google Scholar] [CrossRef]
  124. Allard, V.; Martre, P.; Le Gouis, J. Genetic Variability in Biomass Allocation to Roots in Wheat Is Mainly Related to Crop Tillering Dynamics and Nitrogen Status. Eur. J. Agron. 2013, 46, 68–76. [Google Scholar] [CrossRef]
  125. Tian, G.; Gao, L.; Kong, Y.; Hu, X.; Xie, K.; Zhang, R.; Ling, N.; Shen, Q.; Guo, S. Improving Rice Population Productivity by Reducing Nitrogen Rate and Increasing Plant Density. PLoS ONE 2017, 12, e0182310. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  126. Yang, D.; Cai, T.; Luo, Y.; Wang, Z. Optimizing Plant Density and Nitrogen Application to Manipulate Tiller Growth and Increase Grain Yield and Nitrogen-Use Efficiency in Winter Wheat. PeerJ 2019, 7, e6484. [Google Scholar] [CrossRef] [PubMed]
  127. Xu, J.; Zha, M.; Li, Y.; Ding, Y.; Chen, L.; Ding, C.; Wang, S. The Interaction between Nitrogen Availability and Auxin, Cytokinin, and Strigolactone in the Control of Shoot Branching in Rice (Oryza sativa L.). Plant Cell Rep. 2015, 34, 1647–1662. [Google Scholar] [CrossRef] [PubMed]
  128. Li, X.; Xia, K.; Liang, Z.; Chen, K.; Gao, C.; Zhang, M. MicroRNA393 Is Involved in Nitrogen-Promoted Rice Tillering through Regulation of Auxin Signal Transduction in Axillary Buds. Sci. Rep. 2016, 6, 32158. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  129. Milner, M.J.; Swarbreck, S.M.; Craze, M.; Bowden, S.; Griffiths, H.; Bentley, A.R.; Wallington, E.J. Over-expression of TaDWF4 increases wheat productivity under low and sufficient nitrogen through enhanced carbon assimilation. Commun Biol. 2022, 5, 1–12. [Google Scholar] [CrossRef]
  130. Lu, Z.; Yu, H.; Xiong, G.; Wang, J.; Jiao, Y.; Liu, G.; Jing, Y.; Meng, X.; Hu, X.; Qian, Q.; et al. Genome-Wide Binding Analysis of the Transcription Activator IDEAL PLANT ARCHITECTURE1 Reveals a Complex Network Regulating Rice Plant Architecture. Plant Cell 2013, 25, 3743–3759. [Google Scholar] [CrossRef] [Green Version]
  131. Kazan, K.; Lyons, R. The Link between Flowering Time and Stress Tolerance. J. Exp. Bot. 2016, 67, 47–60. [Google Scholar] [CrossRef] [Green Version]
  132. Gao, H.; Jin, M.; Zheng, X.-M.; Chen, J.; Yuan, D.; Xin, Y.; Wang, M.; Huang, D.; Zhang, Z.; Zhou, K.; et al. Days to Heading 7, a Major Quantitative Locus Determining Photoperiod Sensitivity and Regional Adaptation in Rice. Proc. Natl. Acad. Sci. USA 2014, 111, 16337–16342. [Google Scholar] [CrossRef] [Green Version]
  133. Richards, R.A. Selectable Traits to Increase Crop Photosynthesis and Yield of Grain Crops. J. Exp. Bot. 2000, 51, 447–458. [Google Scholar] [CrossRef]
  134. Weber, K.; Burow, M. Nitrogen–Essential Macronutrient and Signal Controlling Flowering Time. Physiol. Plant. 2018, 162, 251–260. [Google Scholar] [CrossRef]
  135. Hall, A.J.; Savin, R.; Slafer, G.A. Is Time to Flowering in Wheat and Barley Influenced by Nitrogen?: A Critical Appraisal of Recent Published Reports. Eur. J. Agron. 2014, 54, 40–46. [Google Scholar] [CrossRef]
  136. Hill, C.B.; Li, C. Genetic Architecture of Flowering Phenology in Cereals and Opportunities for Crop Improvement. Front. Plant Sci. 2016, 7, 1906. [Google Scholar] [CrossRef] [Green Version]
  137. Quraishi, U.M.; Abrouk, M.; Murat, F.; Pont, C.; Foucrier, S.; Desmaizieres, G.; Confolent, C.; Rivière, N.; Charmet, G.; Paux, E.; et al. Cross-Genome Map Based Dissection of a Nitrogen Use Efficiency Ortho-MetaQTL in Bread Wheat Unravels Concerted Cereal Genome Evolution: Concerted Genome Evolution of a NUE MetaQTL in Cereals. Plant J. 2011, 65, 745–756. [Google Scholar] [CrossRef]
  138. Xu, Q.; Saito, H.; Hirose, I.; Katsura, K.; Yoshitake, Y.; Yokoo, T.; Tsukiyama, T.; Teraishi, M.; Tanisaka, T.; Okumoto, Y. The Effects of the Photoperiod-Insensitive Alleles, Se13, Hd1 and Ghd7, on Yield Components in Rice. Mol. Breed. 2014, 33, 813–819. [Google Scholar] [CrossRef] [Green Version]
  139. Cai, T.; Xu, H.; Peng, D.; Yin, Y.; Yang, W.; Ni, Y.; Chen, X.; Xu, C.; Yang, D.; Cui, Z.; et al. Exogenous Hormonal Application Improves Grain Yield of Wheat by Optimizing Tiller Productivity. Field Crop. Res. 2014, 155, 172–183. [Google Scholar] [CrossRef]
  140. Wu, G.; Wilson, L.T.; McClung, A.M. Contribution of Rice Tillers to Dry Matter Accumulation and Yield. Agron. J. 1998, 90, 317–323. [Google Scholar] [CrossRef]
  141. Forde, B.G.; Lea, P.J. Glutamate in Plants: Metabolism, Regulation, and Signalling. J. Exp. Bot. 2007, 58, 2339–2358. [Google Scholar] [CrossRef]
  142. Bennetzen, J.L.; Ma, J. The Genetic Colinearity of Rice and Other Cereals on the Basis of Genomic Sequence Analysis. Curr. Opin. Plant Biol. 2003, 6, 128–133. [Google Scholar] [CrossRef]
  143. Van Camp, W. Yield Enhancement Genes: Seeds for Growth. Curr. Opin. Biotechnol. 2005, 16, 147–153. [Google Scholar] [CrossRef]
  144. Sorrells, M.E.; La Rota, M.; Bermudez-Kandianis, C.E.; Greene, R.A.; Kantety, R.; Munkvold, J.D.; Miftahudin; Mahmoud, A.; Ma, X.; Gustafson, P.J.; et al. Comparative DNA Sequence Analysis of Wheat and Rice Genomes. Genome Res. 2003, 13, 1818–1827. [Google Scholar] [CrossRef]
  145. The International Wheat Genome Sequencing Consortium (IWGSC); Appels, R.; Eversole, K.; Stein, N.; Feuillet, C.; Keller, B.; Rogers, J.; Pozniak, C.J.; Choulet, F.; Distelfeld, A.; et al. Shifting the Limits in Wheat Research and Breeding Using a Fully Annotated Reference Genome. Science 2018, 361, eaar7191. [Google Scholar] [CrossRef] [Green Version]
  146. Krasileva, K.V.; Vasquez-Gross, H.A.; Howell, T.; Bailey, P.; Paraiso, F.; Clissold, L.; Simmonds, J.; Ramirez-Gonzalez, R.H.; Wang, X.; Borrill, P. Uncovering Hidden Variation in Polyploid Wheat. Proc. Natl. Acad. Sci. USA 2017, 114, E913–E921. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  147. Debernardi, J.M.; Tricoli, D.M.; Ercoli, M.F.; Hayta, S.; Ronald, P.; Palatnik, J.F.; Dubcovsky, J. A GRF–GIF Chimeric Protein Improves the Regeneration Efficiency of Transgenic Plants. Nat. Biotechnol. 2020, 38, 1274–1279. [Google Scholar] [CrossRef] [PubMed]
  148. Shan, Q.; Wang, Y.; Li, J.; Gao, C. Genome Editing in Rice and Wheat Using the CRISPR/Cas System. Nat. Protoc. 2014, 9, 2395–2410. [Google Scholar] [CrossRef]
  149. Wu, D.; Li, Y.; Cao, Y.; Hu, R.; Wu, X.; Zhang, W.; Tao, W.; Xu, G.; Wang, X.; Zhang, Y. Increased Glutamine Synthetase by Overexpression of TaGS1 Improves Grain Yield and Nitrogen Use Efficiency in Rice. Plant Physiol. Biochem. 2021, 169, 259–268. [Google Scholar] [CrossRef] [PubMed]
  150. Nadolska-Orczyk, A.; Rajchel, I.K.; Orczyk, W.; Gasparis, S. Major Genes Determining Yield-Related Traits in Wheat and Barley. Theor. Appl. Genet. 2017, 130, 1081–1098. [Google Scholar] [CrossRef] [Green Version]
  151. Xue, W.; Xing, Y.; Weng, X.; Zhao, Y.; Tang, W.; Wang, L.; Zhou, H.; Yu, S.; Xu, C.; Li, X.; et al. Natural Variation in Ghd7 Is an Important Regulator of Heading Date and Yield Potential in Rice. Nat. Genet. 2008, 40, 761–767. [Google Scholar] [CrossRef]
  152. Distelfeld, A.; Li, C.; Dubcovsky, J. Regulation of Flowering in Temperate Cereals. Curr. Opin. Plant Biol. 2009, 12, 178–184. [Google Scholar] [CrossRef] [Green Version]
  153. Distelfeld, A.; Tranquilli, G.; Li, C.; Yan, L.; Dubcovsky, J. Genetic and Molecular Characterization of the VRN2 Loci in Tetraploid Wheat. Plant Physiol. 2009, 149, 245–257. [Google Scholar] [CrossRef] [Green Version]
  154. Rubio-Asensio, J.S.; Bloom, A.J. Inorganic Nitrogen Form: A Major Player in Wheat and Arabidopsis Responses to Elevated CO2. J. Exp. Bot. 2016, 68, 2611–2625. [Google Scholar] [CrossRef]
  155. Wood, C.C.; Porée, F.; Dreyer, I.; Koehler, G.J.; Udvardi, M.K. Mechanisms of Ammonium Transport, Accumulation, and Retention in Ooyctes and Yeast Cells Expressing Arabidopsis AtAMT1; 1. FEBS Lett. 2006, 580, 3931–3936. [Google Scholar] [CrossRef] [Green Version]
  156. Wang, R.; Okamoto, M.; Xing, X.; Crawford, N.M. Microarray Analysis of the Nitrate Response in Arabidopsis Roots and Shoots Reveals over 1,000 Rapidly Responding Genes and New Linkages to Glucose, Trehalose-6-Phosphate, Iron, and Sulfate Metabolism. Plant Physiol. 2003, 132, 556–567. [Google Scholar] [CrossRef] [Green Version]
  157. Bloom, A.J. The Increasing Importance of Distinguishing among Plant Nitrogen Sources. Curr. Opin. Plant Biol. 2015, 25, 10–16. [Google Scholar] [CrossRef]
  158. Zhang, J.; Liu, Y.-X.; Zhang, N.; Hu, B.; Jin, T.; Xu, H.; Qin, Y.; Yan, P.; Zhang, X.; Guo, X.; et al. NRT1.1B Is Associated with Root Microbiota Composition and Nitrogen Use in Field-Grown Rice. Nat. Biotechnol. 2019, 37, 676–684. [Google Scholar] [CrossRef]
  159. Katz, E.; Knapp, A.; Lensink, M.; Keller, C.K.; Stefani, J.; Li, J.J.; Shane, E.; Tuermer-Lee, K.; Bloom, A.J.; Kliebenstein, D.J. Genetic variation underlying differential ammonium and nitrate responses in Arabidopsis thaliana. The Plant Cell. 2022, 34, 4696–4713. [Google Scholar] [CrossRef]
  160. Carlisle, E.; Myers, S.; Raboy, V.; Bloom, A. The Effects of Inorganic Nitrogen Form and CO2 Concentration on Wheat Yield and Nutrient Accumulation and Distribution. Front. Plant Sci. 2012, 3, 195. [Google Scholar] [CrossRef] [Green Version]
  161. Wang, W.; Hu, B.; Li, A.; Chu, C. NRT1.1s in Plants: Functions beyond Nitrate Transport. J. Exp. Bot. 2020, 71, 4373–4379. [Google Scholar] [CrossRef]
  162. Kanstrup, C.; Nour-Eldin, H.H. The Emerging Role of the Nitrate and Peptide Transporter Family: NPF in Plant Specialized Metabolism. Curr. Opin. Plant Biol. 2022, 68, 102243. [Google Scholar] [CrossRef]
  163. Hachiya, T.; Sakakibara, H. Interactions between Nitrate and Ammonium in Their Uptake, Allocation, Assimilation, and Signaling in Plants. J. Exp. Bot. 2016, 68, 2501–2512. [Google Scholar] [CrossRef]
  164. Léran, S.; Varala, K.; Boyer, J.-C.; Chiurazzi, M.; Crawford, N.; Daniel-Vedele, F.; David, L.; Dickstein, R.; Fernandez, E.; Forde, B.; et al. A Unified Nomenclature of NITRATE TRANSPORTER 1/PEPTIDE TRANSPORTER Family Members in Plants. Trends Plant Sci. 2014, 19, 5–9. [Google Scholar] [CrossRef]
  165. Feng, H.; Yan, M.; Fan, X.; Li, B.; Shen, Q.; Miller, A.J.; Xu, G. Spatial Expression and Regulation of Rice High-Affinity Nitrate Transporters by Nitrogen and Carbon Status. J. Exp. Bot. 2011, 62, 2319–2332. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  166. Araki, R.; Hasegawa, H. Expression of Rice (Oryza sativa L.) Genes Involved in High-Affinity Nitrate Transport during the Period of Nitrate Induction. Breed. Sci. 2006, 56, 295–302. [Google Scholar] [CrossRef] [Green Version]
  167. Konishi, N.; Ma, J.F. Three Polarly Localized Ammonium Transporter 1 Members Are Cooperatively Responsible for Ammonium Uptake in Rice under Low Ammonium Condition. New Phytol. 2021, 232, 1778–1792. [Google Scholar] [CrossRef] [PubMed]
  168. Li, C.; Tang, Z.; Wei, J.; Qu, H.; Xie, Y.; Xu, G. The OsAMT1.1 Gene Functions in Ammonium Uptake and Ammonium–Potassium Homeostasis over Low and High Ammonium Concentration Ranges. J. Genet. Genom. 2016, 43, 639–649. [Google Scholar] [CrossRef] [PubMed]
  169. Yamaya, T.; Kusano, M. Evidence Supporting Distinct Functions of Three Cytosolic Glutamine Synthetases and Two NADH-Glutamate Synthases in Rice. J. Exp. Bot. 2014, 65, 5519–5525. [Google Scholar] [CrossRef] [Green Version]
  170. Pereira, E.G.; Bucher, C.P.C.; Bucher, C.A.; Santos, L.A.; Lerin, J.; Catarina, C.S.; Fernandes, M.S. The Amino Acid Transporter OsAAP1 Regulates the Fertility of Spikelets and the Efficient Use of N in Rice. Plant Soil 2022, 480, 507–521. [Google Scholar] [CrossRef]
  171. Yang, M.; Lu, K.; Zhao, F.-J.; Xie, W.; Ramakrishna, P.; Wang, G.; Du, Q.; Liang, L.; Sun, C.; Zhao, H.; et al. Genome-Wide Association Studies Reveal the Genetic Basis of Ionomic Variation in Rice. Plant Cell 2018, 30, 2720–2740. [Google Scholar] [CrossRef] [Green Version]
  172. Chen, Y.; Chen, Y.; Zhang, Y.; Zhang, D.; Li, G.; Wei, J.; Hua, X.; Lv, B.; Liu, L. Heterotrimeric G Protein γ Subunit DEP1 Is Involved in Hydrogen Peroxide Signaling and Promotes Aerenchyma Formation in Rice Roots. Plant Signal. Behav. 2021, 16, 1889251. [Google Scholar] [CrossRef]
  173. Ainsworth, E.A.; Long, S.P. 30 Years of Free-air Carbon Dioxide Enrichment (FACE): What Have We Learned about Future Crop Productivity and Its Potential for Adaptation? Glob. Chang. Biol. 2020, 27, 27–49. [Google Scholar] [CrossRef]
  174. Myers, S.S.; Zanobetti, A.; Kloog, I.; Huybers, P.; Leakey, A.D.B.; Bloom, A.J.; Carlisle, E.; Dietterich, L.H.; Fitzgerald, G.; Hasegawa, T.; et al. Increasing CO2 Threatens Human Nutrition. Nature 2014, 510, 139–142. [Google Scholar] [CrossRef]
  175. Jauregui, I.; Aroca, R.; Garnica, M.; Zamarreño, Á.M.; García-Mina, J.M.; Serret, M.D.; Parry, M.; Irigoyen, J.J.; Aranjuelo, I. Nitrogen Assimilation and Transpiration: Key Processes Conditioning Responsiveness of Wheat to Elevated [CO2] and Temperature. Physiol. Plant. 2015, 155, 338–354. [Google Scholar] [CrossRef]
  176. Long, S.P.; Ainsworth, E.A.; Rogers, A.; Ort, D.R. RISING ATMOSPHERIC CARBON DIOXIDE: Plants FACE the Future. Annu. Rev. Plant Biol. 2004, 55, 591–628. [Google Scholar] [CrossRef]
  177. Luo, Y.; Su, B.O.; Currie, W.S.; Dukes, J.S.; Finzi, A.; Hartwig, U.; Hungate, B.; Mc Murtrie, R.E.; Oren, R.A.M.; Parton, W.J. Progressive Nitrogen Limitation of Ecosystem Responses to Rising Atmospheric Carbon Dioxide. BioScience 2004, 54, 731–739. [Google Scholar] [CrossRef] [Green Version]
  178. Taub, D.R.; Wang, X. Why Are Nitrogen Concentrations in Plant Tissues Lower under Elevated CO2? A Critical Examination of the Hypotheses. J. Integr. Plant Biol. 2008, 50, 1365–1374. [Google Scholar] [CrossRef]
  179. Bloom, A.J.; Smart, D.R.; Nguyen, D.T.; Searles, P.S. Nitrogen Assimilation and Growth of Wheat under Elevated Carbon Dioxide. Proc. Natl. Acad. Sci. USA 2002, 99, 1730–1735. [Google Scholar] [CrossRef] [Green Version]
  180. Gojon, A.; Cassan, O.; Bach, L.; Lejay, L.; Martin, A. The Decline of Plant Mineral Nutrition under Rising CO2: Physiological and Molecular Aspects of a Bad Deal. Trends Plant Sci. 2022, in press. [CrossRef]
  181. Wang, L.; Feng, Z.; Schjoerring, J.K. Effects of Elevated Atmospheric CO 2 on Physiology and Yield of Wheat (Triticum aestivum L.): A Meta-Analytic Test of Current Hypotheses. Agric. Ecosyst. Environ. 2013, 178, 57–63. [Google Scholar] [CrossRef]
  182. Pleijel, H.; Uddling, J. Yield vs. Quality Trade-offs for Wheat in Response to Carbon Dioxide and Ozone. Glob. Chang. Biol. 2012, 18, 596–605. [Google Scholar] [CrossRef]
  183. Bloom, A.J.; Burger, M.; Asensio, J.S.R.; Cousins, A.B. Carbon Dioxide Enrichment Inhibits Nitrate Assimilation in Wheat and Arabidopsis. Science 2010, 328, 899–903. [Google Scholar] [CrossRef] [Green Version]
  184. Bloom, A.J.; Burger, M.; Kimball, B.A.; Pinter, J., Jr. Nitrate assimilation is inhibited by elevated CO2 in field-grown wheat. Nat. Clim. Chang. 2014, 4, 477–480. [Google Scholar] [CrossRef]
  185. Subbarao, G.V.; Searchinger, T.D. Opinion: A “More Ammonium Solution” to Mitigate Nitrogen Pollution and Boost Crop Yields. Proc. Natl. Acad. Sci. USA 2021, 118, e2107576118. [Google Scholar] [CrossRef] [PubMed]
  186. Subbarao, G.V.; Rondon, M.; Ito, O.; Ishikawa, T.; Rao, I.M.; Nakahara, K.; Lascano, C.; Berry, W.L. Biological Nitrification Inhibition (BNI)—Is It a Widespread Phenomenon? Plant Soil 2007, 294, 5–18. [Google Scholar] [CrossRef]
  187. Subbarao, G.V.; Kishii, M.; Bozal-Leorri, A.; Ortiz-Monasterio, I.; Gao, X.; Ibba, M.I.; Karwat, H.; Gonzalez-Moro, M.B.; Gonzalez-Murua, C.; Yoshihashi, T.; et al. Enlisting Wild Grass Genes to Combat Nitrification in Wheat Farming: A Nature-Based Solution. Proc. Natl. Acad. Sci. USA 2021, 118, e2106595118. [Google Scholar] [CrossRef] [PubMed]
  188. Masclaux-Daubresse, C.; Daniel-Vedele, F.; Dechorgnat, J.; Chardon, F.; Gaufichon, L.; Suzuki, A. Nitrogen Uptake, Assimilation and Remobilization in Plants: Challenges for Sustainable and Productive Agriculture. Ann. Bot. 2010, 105, 1141–1157. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  189. Atwell, S.; Huang, Y.S.; Vilhjálmsson, B.J.; Willems, G.; Horton, M.; Li, Y.; Meng, D.; Platt, A.; Tarone, A.M.; Hu, T.T. Genome-Wide Association Study of 107 Phenotypes in Arabidopsis Thaliana Inbred Lines. Nature 2010, 465, 627–631. [Google Scholar] [CrossRef] [Green Version]
  190. Nordborg, M.; Weigel, D. Next-Generation Genetics in Plants. Nature 2008, 456, 720–723. [Google Scholar] [CrossRef]
  191. Lopez-Arboleda, W.A.; Reinert, S.; Nordborg, M.; Korte, A. Global Genetic Heterogeneity in Adaptive Traits. Mol. Biol. Evol. 2021, 38, 4822–4831. [Google Scholar] [CrossRef]
  192. Sandhu, N.; Sethi, M.; Kumar, A.; Dang, D.; Singh, J.; Chhuneja, P. Biochemical and Genetic Approaches Improving Nitrogen Use Efficiency in Cereal Crops: A Review. Front. Plant Sci. 2021, 12, 657629. [Google Scholar] [CrossRef]
  193. Wang, Q.; Tang, J.; Han, B.; Huang, X. Advances in Genome-Wide Association Studies of Complex Traits in Rice. Theor. Appl. Genet. 2020, 133, 1415–1425. [Google Scholar] [CrossRef]
  194. Brachi, B.; Morris, G.P.; Borevitz, J.O. Genome-Wide Association Studies in Plants: The Missing Heritability Is in the Field. Genome Biol. 2011, 12, 232. [Google Scholar] [CrossRef]
  195. Alonso-Blanco, C.; Aarts, M.G.M.; Bentsink, L.; Keurentjes, J.J.B.; Reymond, M.; Vreugdenhil, D.; Koornneef, M. What Has Natural Variation Taught Us about Plant Development, Physiology, and Adaptation? Plant Cell 2009, 21, 1877–1896. [Google Scholar] [CrossRef] [Green Version]
  196. Bhat, J.A.; Yu, D.; Bohra, A.; Ganie, S.A.; Varshney, R.K. Features and Applications of Haplotypes in Crop Breeding. Commun. Biol. 2021, 4, 1266. [Google Scholar] [CrossRef]
  197. Varshney, R.K.; Bohra, A.; Yu, J.; Graner, A.; Zhang, Q.; Sorrells, M.E. Designing Future Crops: Genomics-Assisted Breeding Comes of Age. Trends Plant Sci. 2021, 26, 631–649. [Google Scholar] [CrossRef]
  198. Wadgymar, S.M.; DeMarche, M.L.; Josephs, E.B.; Sheth, S.N.; Anderson, J.T. Local Adaptation: Causal Agents of Selection and Adaptive Trait Divergence. Annu. Rev. Ecol. Evol. Syst. 2022, 53, 87–111. [Google Scholar] [CrossRef]
  199. Xie, W.; Wang, G.; Yuan, M.; Yao, W.; Lyu, K.; Zhao, H.; Yang, M.; Li, P.; Zhang, X.; Yuan, J.; et al. Breeding Signatures of Rice Improvement Revealed by a Genomic Variation Map from a Large Germplasm Collection. Proc. Natl. Acad. Sci. USA 2015, 112, E5411–E5419. [Google Scholar] [CrossRef] [Green Version]
  200. Nicotra, A.B.; Atkin, O.K.; Bonser, S.P.; Davidson, A.M.; Finnegan, E.J.; Mathesius, U.; Poot, P.; Purugganan, M.D.; Richards, C.L.; Valladares, F.; et al. Plant Phenotypic Plasticity in a Changing Climate. Trends Plant Sci. 2010, 15, 684–692. [Google Scholar] [CrossRef]
  201. Hawkesford, M.J.; Riche, A.B. Impacts of G x E x M on Nitrogen Use Efficiency in Wheat and Future Prospects. Front. Plant Sci. 2020, 11, 1157. [Google Scholar] [CrossRef]
  202. Gao, C. Genome Engineering for Crop Improvement and Future Agriculture. Cell 2021, 184, 1621–1635. [Google Scholar] [CrossRef]
  203. Li, X.; Guo, T.; Wang, J.; Bekele, W.A.; Sukumaran, S.; Vanous, A.E.; McNellie, J.P.; Tibbs-Cortes, L.E.; Lopes, M.S.; Lamkey, K.R.; et al. An Integrated Framework Reinstating the Environmental Dimension for GWAS and Genomic Selection in Crops. Mol. Plant 2021, 14, 874–887. [Google Scholar] [CrossRef]
  204. Soyk, S.; Benoit, M.; Lippman, Z.B. New Horizons for Dissecting Epistasis in Crop Quantitative Trait Variation. Annu. Rev. Genet. 2020, 54, 287–307. [Google Scholar] [CrossRef]
  205. Wei, X.; Qiu, J.; Yong, K.; Fan, J.; Zhang, Q.; Hua, H.; Liu, J.; Wang, Q.; Olsen, K.M.; Han, B.; et al. A Quantitative Genomics Map of Rice Provides Genetic Insights and Guides Breeding. Nat. Genet. 2021, 53, 243–253. [Google Scholar] [CrossRef] [PubMed]
  206. Brevis, J.C.; Dubcovsky, J. Effects of the Chromosome Region Including the Gpc-B1 Locus on Wheat Grain and Protein Yield. Crop Sci. 2010, 50, 93–104. [Google Scholar] [CrossRef] [Green Version]
  207. Tabbita, F.; Pearce, S.; Barneix, A.J. Breeding for Increased Grain Protein and Micronutrient Content in Wheat: Ten Years of the GPC-B1 Gene. J. Cereal Sci. 2017, 73, 183–191. [Google Scholar] [CrossRef]
  208. Wang, X.; Yang, G.; Shi, M.; Hao, D.; Wei, Q.; Wang, Z.; Fu, S.; Su, Y.; Xia, J. Disruption of an Amino Acid Transporter LHT1 Leads to Growth Inhibition and Low Yields in Rice. BMC Plant Biol. 2019, 19, 268. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  209. Guo, N.; Gu, M.; Hu, J.; Qu, H.; Xu, G. Rice OsLHT1 Functions in Leaf-to-Panicle Nitrogen Allocation for Grain Yield and Quality. Front. Plant Sci. 2020, 11, 1150. [Google Scholar] [CrossRef]
  210. Swarbreck, S.M.; Wang, M.; Wang, Y.; Kindred, D.; Sylvester-Bradley, R.; Shi, W.; Varinderpal-Singh; Bentley, A.R.; Griffiths, H. A Roadmap for Lowering Crop Nitrogen Requirement. Trends Plant Sci. 2019, 24, 892–904. [Google Scholar] [CrossRef]
  211. Hawkesford, M.J.; Araus, J.; Park, R.; Calderini, D.; Miralles, D.; Shen, T.; Zhang, J.; Parry, M.A.J. Prospects of Doubling Global Wheat Yields. Food Energy Secur. 2013, 2, 34–48. [Google Scholar] [CrossRef]
  212. Simmonds, N.W. The Relation between Yield and Protein in Cereal Grain. J. Sci. Food Agric. 1995, 67, 309–315. [Google Scholar] [CrossRef]
  213. Wu, W.; Wang, Y.; Wang, L.; Xu, H.; Zörb, C.; Geilfus, C.-M.; Xue, C.; Sun, Z.; Ma, W. Booting Stage Is the Key Timing for Split Nitrogen Application in Improving Grain Yield and Quality of Wheat–A Global Meta-Analysis. Field Crop. Res. 2022, 287, 108665. [Google Scholar] [CrossRef]
  214. Michel, S.; Löschenberger, F.; Ametz, C.; Pachler, B.; Sparry, E.; Bürstmayr, H. Simultaneous Selection for Grain Yield and Protein Content in Genomics-Assisted Wheat Breeding. Theor. Appl. Genet. 2019, 132, 1745–1760. [Google Scholar] [CrossRef]
Table
Table 1. Average contribution of wheat and rice to daily protein intake between 2010 and 2019 [19].
Table 1. Average contribution of wheat and rice to daily protein intake between 2010 and 2019 [19].
RegionProtein Supply in FAO’s Food Balance Sheet (g per Capita per Day)
Total IntakeFrom Wheat%From Rice%% Wheat and Rice
World 81.3916.2820.0010.0812.3832.38
AfricaEastern Africa60.066.3110.503.355.5816.09
Middle Africa45.173.507.752.395.2913.04
Northern Africa93.0535.3337.973.453.7141.68
Southern Africa78.9113.7617.442.773.5120.95
Western Africa63.465.027.927.8712.4020.32
AmericaCaribbean67.319.4013.979.3713.9327.89
Central America83.736.868.192.052.4510.64
Northern America110.5719.3117.461.441.3018.76
South America86.5311.8913.745.536.4020.13
AsiaCentral Asia91.2935.1438.491.261.3839.87
Eastern Asia98.3517.2917.5814.8915.1432.72
South-eastern Asia69.054.826.9923.5234.0741.05
Southern Asia62.7116.4826.2913.0920.8847.17
Western Asia87.6734.3339.163.724.2443.40
EuropeEastern Europe97.6828.7729.450.690.7030.15
Northern Europe106.6024.2722.761.131.0623.83
Southern Europe104.5926.9125.731.251.1926.92
Western Europe105.4323.5422.330.830.7923.12
OceaniaAustralia/New Zealand106.2318.9317.821.731.6319.45
Melanesia65.038.4012.923.745.7518.67
Micronesia71.2810.8615.2312.6017.6832.91
Polynesia92.7614.2515.364.174.5019.86
Table
Table 2. Breeding applications of nitrogen metabolism genes in rice and wheat that were proven successful in improving yield.
Table 2. Breeding applications of nitrogen metabolism genes in rice and wheat that were proven successful in improving yield.
#GeneRefSpeciesBreeding ApplicationYield Component Improvement
YieldTilleringGrain weightGrain numberBiomass NUEOther/Note
1Nitrate transporter 1/Peptide transporter Family 6.1NPF6.1[55]RiceOverexpression✓✓ * * Effective panicle number. No data on grain number and weight.
2Nitrate transporter 1/Peptide transporter Family 6.3NPF6.3 (NRT1.1A)[56]RiceOverexpression✓ ✓Shortened maturation time
3Nitrate transporter 1/Peptide transporter Family 6.5NPF6.5 (NRT1.1B)[57]RiceNear-isogenic line, Transgenic japonica with indica variant✓✓ ✓
4Nitrate transporter 1/Peptide transporter Family 7.1NPF7.1[58]RiceOverexpression✓✓ ✓✓
5Nitrate transporter 1/Peptide transporter Family 7.1NPF7.2[59]RiceOverexpression✓✓ Increased root length, root number, root biomass
6Nitrate transporter 1/Peptide transporter Family 7.4NPF7.4[58]RiceCRISPR/Cas9 mutant✓✓ ✓✓
7Nitrate transporter 1/Peptide transporter Family 7.7NPF7.7[60]RiceOverexpression✓ *✓ ✓✓* Yield presented as g grain/g N. Larger panicle, Higher N content, but not amino acid suggests N accumulation
8Nitrate transporter 1/Peptide transporter Family 8.20NPF8.20 (PTR9)[61]RiceOverexpression✓✓✓✓✓Highest improvement at low N
9High-affinity nitrate transporter 2.1NRT2.1[62,63]RiceOverexpression✓ Increased Mn accumulation
10High-affinity nitrate transporter 2.3bNRT2.3b[64]RiceOverexpression✓ ✓
11High-affinity nitrate transporter-activating protein 2.1NAR2.1[63,65,66,67]RiceOverexpression, Transgenic with native promoter✓ ✓
12Ammonium transporter 1;1AMT1;1[68]RiceOverexpression✓
13Ammonium transporter 1;2AMT1;2[69]RiceDouble activation mutants with GOGAT1✓ ✓
14Glutamate synthase 1GOGAT1[69]RiceDouble activation mutants with AMT1;2✓ ✓
15Glutamine synthetase 1GS1[70]RiceOverexpression✓✓ ✓
16Glutamine synthetase 2GS2[70,71]Wheat, RiceOverexpression✓ ✓✓
17Nitrate reductase 2NR2[72]RiceTransgenic japonica with indica variant✓✓ ✓
18Asparagine synthetase 1ASN1[73]RiceOverexpression✓ * ✓* Yield increases only at low N
19Amino acid permease 1AAP1[74]RiceOverexpression✓✓ ✓
20Amino acid permease 3AAP3[75]RiceRNAi✓✓
21Amino acid permease 4AAP4[76]RiceOverexpression✓✓
22Amino acid permease 5AAP5[77]RiceRNAi✓✓
23Nodule Inception-Like protein 1NLP1[78]RiceOverexpression✓ ✓
24Nodule Inception-Like protein 3NLP3[79]RiceOverexpression✓ ✓
25Nodule Inception-Like protein 4NLP4[80,81]RiceOverexpression, Quadrupling the promoter of NiR✓✓
26Growth-Regulating Factor 4GRF4[42]Rice, WheatOverexpression✓✓ *✓✓ *✓* Depending on genetic background; Tiller number not changed in dep1-driven dwarfism and in NJ6-sd1. Grain number not changed in NJ6-sd1
27Nitrogen-mediated tiller Growth Response 5NGR5[82]RiceOverexpression✓✓???Did not show changes in yield components apart from tiller number for NGR5 transgenic plants.
28Teosinte branched1, Cycloidea, Proliferating cell factor 19TCP19[83]RiceIntrogression✓✓ ✓
29NAM/ATAF1/2/CUC2 42NAC42[55]RiceSee NPF6.1
30NAM/ATAF1/2/CUC2 2-5ANAC2-5A[84]WheatOverexpression✓ ✓Increased root growth
31Basic Leucine Zipper 60bZIP60[85]WheatRNAi✓ ✓ Increased N uptake and NADH-GOGAT
32Grain Number, Plant Height, and Heading Date 7Ghd7[86]RiceOverexpression✓ ✓
33Abnormal cytokinin response1 REpressor 1ARE1[87,88]Rice, WheatLoss-of-function, CRISPR/Cas9 mutant✓ Delayed senescence
34N-mediated heading date 1Nhd1[89,90]RiceKnockout mutant✓✓ ✓Increased N uptake
35Dense and Erect Panicle 1DEP1[91,92,93]RiceLoss-of-function, gain of function mutant✓ ✓
36Dull Nitrogen Response 1DNR1[94]RiceLoss-of-function mutant✓ ✓ Lower tiller number. Total biomass/grain weight not reported.
37Dehydration-Responsive Element-Binding Protein 1CDREB1C[95]Rice, WheatOverexpression✓ ✓✓✓Early flowering, Higher photosynthesis, Higher N uptake, Higher harvest index
38Nuclear Factor Y A-B1NFYA-B1[96]WheatOverexpression✓✓ Higher N uptake, Higher grain N content, More lateral root growth
39Rice Dof Daily fluctuations 1RDD1[97]RiceOverexpression✓ ✓✓ Higher harvest index, Altered uptake of multiple nutrients, Early flowering
40MicroRNA 396MIR396[98]RiceKnock-out mutant✓ ✓✓✓15% yield increases under low N, Larger panicle
Table
Table 3. Functions, selection, and effects on nitrogen acquisition of nitrogen metabolism genes in rice and wheat that were proven successful in improving yield. Empty cells indicate the lack of information.
Table 3. Functions, selection, and effects on nitrogen acquisition of nitrogen metabolism genes in rice and wheat that were proven successful in improving yield. Empty cells indicate the lack of information.
#GeneRefFunctionNatural Variation and SelectionEffects on NH4+Effects on NO3−
1NPF6.1[55]NO3− uptake; Must be activated by NAC42 transcriptional factorRare allele absent in 90.3% of rice varieties Increased uptake/concentration
2NPF6.3 (NRT1.1A)[56]Upregulate N utilization and flowering genes
3NPF6.5 (NRT1.1B)[57,158]NO3− uptake, transporter; Upregulate NO3− responsive genesDirectional positive selection. Indica has a functional variant.
4NPF7.1[58]Determine axillary bud outgrowth; NO3− uptake Increased influx/concentration
5NPF7.2[59]Upregulate genes in cytokinin pathway, thereby increasing cytokinin concentration; Downregulate genes in strigolactone biosynthesis, perception and signaling pathway, thereby reducing suppression on tillering. Increased influx/concentration
6NPF7.4[58]Determine axillary bud outgrowth, NO3− uptake Increased influx/concentration
7NPF7.7[60]Two splicing variants transport distinct N forms; Upregulate GS1.2, NPF6.5; Downregulate Fine Culm1 (FC1), Dwarf3 (D3) to regulate tillering. Increased influx/concentration for both variant, Higher for variant 2Increased influx/concentration for variant 1 only
8NPF8.20 (PTR9)[61,164]Upregulate GS, AMT1;2; Increase lateral root density Increased uptake
9NRT2.1[62,63]High affinity NO3− transporter; Responsive only to NO3−; Interact with NAR2.1.
10NRT2.3b[64,165]Buffering pH; NO3− uptake; Increase NH4+ uptake even though it does not transport NH4+Under selection. Expression ratio of two variants correlated with vegetative N content. Increased uptake
11NAR2.1[63,65,66,67,165,166]NO3− uptake, interacting with NRT2.1, NRT2.2, NRT2.3a Increased uptake
12AMT1;1[68,167,168]NH4+ uptake under low and high NH4+ conditions; N/K homeostasis Increased uptake
13AMT1;2[69,167]NH4+ uptake and remobilization
14GOGAT1[69]NH4+ uptake and remobilization
15GS1[70,169]Coordinate N metabolic balance and remobilization; Confer tolerance to abiotic stresses; Must be expressed concurrently with GS2.
16GS2[70,71]Increase root N uptake before and after flowering, N mobilization and N harvest index; Prolong leaf photosynthesis post-anthesis; Increase expression of NRT2.1 and NPF 6.3; In rice, must be expressed concurrently with GS1.2 haplotypes in A genome
17NR2[72]Encode NADH/NADPH-dependent NO3− reductase; Interact with NPF6.5 to control NO3− uptakeDiverged between indica and japonica. Increased uptake
18ASN1[73]Upregulate AMT1;1, AMT1;2, AMT1;3, GS1;1, NADH-GOGAT1 Increased uptake
19AAP1[74,170]Facilitate amino acid transportation to reproductive organs
20AAP3[75]Reduced expression promotes tiller bud elongation, relatively more than formation, via balancing basic and neutral amino acid to maintain higher cytokinin25 haplotypes. Promoter sequence differs between indica and japonica
21AAP4[76]Higher expression in indica produce more tiller and grain yield5 haplotypes. Promoter sequence differs between indica and japonica
22AAP5[77]Reduced expression regulate tiller bud via balancing basic and neutral amino acid to maintain higher cytokinin11 promoter variants. Sequence differs between indica and japonica
23NLP1[78]Regulate transcription of N related genes and transcriptional factors (both NO3− and NH4+)
24NLP3[79]Bind to NO3−-responsive cis-elements in promoters of N uptake and assimilation genes; Overlaps with NLP1 and NLP4
25NLP4[80,81]Regulate expression of known N genes by binding to NO3−- responsive cis-element in promoter, Activate NiR2 haplotypes
26GRF4[42]Counteracts DELLA to promote N assimilation both NO3− and NH4+; Upregulate expression of AMT1.1, GS1.2, GS2, NADH-GOGAT2, NRT1.1B, NRT2.3a, NPF2.4, NIA1, NIA3, NiR1 and genes related to photosynthesis, C metabolism and cell division to maintain stable C:N ratio; Highest expression at low N;3 haplotypes. Haplotype B has highest yield. Absent from elite varieties.Increased uptakeIncreased uptake
27NGR5[82]Recruit PRC2 upon increased N supply to promote H3K27me3 modification that represses shoot branching inhibitory genes; DELLA proteins stabilize NGR5 and sustain tiller promotion by competitively inhibiting gibberillin-driven destruction of NGR5.5 haplotypes. Haplotype 2 contains a functional variant.
28TCP19[83]Represses tiller promoting gene DLT, the product of which can interact directly with NGR5, to negatively control cellular bud growth
29NAC42[55]Activate NPF6.1
30NAC2-5A[84]Regulate expression of NO3− transporter and GS Increased uptake
31bZIP60[85]Negative regulation on NADH-GOGAT
32Ghd7[86,171]Repress ARE1 to positively regulate N utilizationAt least 10 alleic variants. Alleic frequency correlates with N deposition rate.
33ARE1[87,88]Suppress Fd-GOGAT3 haplotypes in promoter sequence. Under selection.
34Nhd1[89,90]Activate Hd3a for flowering time; Control negative feedback on N assimilation (loss-of-function increases Fd-GOGAT and LHT1 activities); Activate AMT1;3, NRT2.45 haplotypes. Similar between indica and japonica. Variation in promoter associated with nitrogen.
35DEP1[91,92,93,172]Interact to reduce heterotrimeric G-protein α-subunit 1 (RGA1) or enhance β-subunit 1 (RGB1) to inhibit N responses; Promote aerenchyma formation; Upregulate GS/GOGATUnder selection during japonica domestication.
36DNR1[94]Negative regulator of auxin-regulated N metabolism; N supply lowers DNR1, thereby inducing Auxin Response Factors to upregulate NPF6.5, NRT2.3a, NPF2.4, and NIA2.3 haplotypes. Haplotype A specific to indica is absent from japonica. Increased uptake
37DREB1C[95]Regulate NT2, NRT2.4, NPF6.53 haplotypes in promoter sequence. Haplotype 3 superior. Increased uptake
38NFYA-B1[96]Control root development and N, P usage Increased uptake
39RDD1[97]Upregulate AMT1;3, GS1;1; Uptake of N, P, K, Na, Mg, Cl, S, CaHighly conserved in wild rice relatives.Increased uptake/accumulationIncreased uptake
40MIR396[98]Only isoform e and f; Upregulate GRF4, GRF6, GRF8, NIR1, NIR2, GOGAT2, GS1.2, AAPs
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Kasemsap, P.; Bloom, A.J. Breeding for Higher Yields of Wheat and Rice through Modifying Nitrogen Metabolism. Plants 2023, 12, 85. https://doi.org/10.3390/plants12010085

AMA Style

Kasemsap P, Bloom AJ. Breeding for Higher Yields of Wheat and Rice through Modifying Nitrogen Metabolism. Plants. 2023; 12(1):85. https://doi.org/10.3390/plants12010085

Chicago/Turabian Style

Kasemsap, Pornpipat, and Arnold J. Bloom. 2023. "Breeding for Higher Yields of Wheat and Rice through Modifying Nitrogen Metabolism" Plants 12, no. 1: 85. https://doi.org/10.3390/plants12010085

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