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Review

Advances in Understanding the Molecular Mechanisms and Potential Genetic Improvement for Nitrogen Use Efficiency in Barley

by
Sakura D. Karunarathne
1,2,
Yong Han
1,2,
Xiao-Qi Zhang
1,2 and
Chengdao Li
1,2,3,*
1
Western Barley Genetics Alliance, College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch WA 6150, Australia
2
Western Australian State Agricultural Biotechnology Centre, Murdoch University, 90 South Street, Murdoch WA 6150, Australia
3
Department of Primary Industries and Regional Development, 3-Baron-Hay Court, South Perth WA 6151, Australia
*
Author to whom correspondence should be addressed.
Agronomy 2020, 10(5), 662; https://doi.org/10.3390/agronomy10050662
Submission received: 13 April 2020 / Revised: 6 May 2020 / Accepted: 6 May 2020 / Published: 8 May 2020
(This article belongs to the Special Issue Molecular Genetics, Genomics and Breeding of Cereal Crops)
Browse Figure
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Abstract

:
Nitrogen (N) fertilization plays an important role in crop production; however, excessive and inefficient use of N fertilizer is a global issue that incurs high production costs, pollutes the environment and increases the emission of greenhouse gases. To overcome these negative consequences, improving nitrogen use efficiency (NUE) would be a key factor for profitable crop production either by increasing yield or reducing fertilizer cost. In contrast to soil and crop management practices, understanding the molecular mechanisms in NUE and developing new varieties with improved NUE is more environmentally and economically friendly. In this review, we highlight the recent progress in understanding and improving nitrogen use efficiency in barley, with perspectives on the impact of N on plant morphology and agronomic performance, NUE and its components such as N uptake and utilization, QTLs and candidate genes controlling NUE, and new strategies for NUE improvement.

1. Introduction

Soil nitrogen (N) availability usually limits plant yields such that large quantities of synthetic N fertilizers are applied to ensure maximum productivity. However, excessive N use is a significant issue around the world. For example, NPK fertilizer use in China increased from 0.73 million tons in 1961 to 54.16 million tons in 2015 [1,2]. The Food and Agriculture Organization of the United Nations estimated that N consumption would be around 119 million tons by 2020 with the increased population growth and demand for food [3]. Based on available data, N fertilizer demand is expected to increase by 1.2% per annum until 2022 [4]. Although high rates of N are applied, crop absorption is most likely 30%–50% [5]. The remaining N is leached into the environment and soil or lost through surface runoff and erosion. Consequently, N residues cause considerable adverse effects on the environment and human health by water, soil and air pollution. They contaminate groundwater, deplete the ozone layer and increase greenhouse gas levels (i.e., N2O), causing global warming [6,7]. Thus, developing crop varieties with improved nitrogen use efficiency (NUE) that require fewer N inputs is economically and environmentally favourable to maintain the same or higher grain yields.
There are two major approaches to improving NUE, viz genotypic improvement through conventional breeding and genetic improvement through manipulating specific NUE-associated genes. Several studies have been undertaken to improve NUE in crops including rice, wheat and maize [8,9,10,11]. Starting from simple phenotypic screening through to advanced molecular techniques, crop performance under low N has been improved [12,13]. There are a few success stories for rice NUE improvement by genetic engineering [14,15,16]. For instance, overexpression of alanine aminotransferase in both rice and canola under a tissue-specific promoter increased yield under low N [14,17]. Similarly, the overexpression of nitrate transporters increased grain yield and NUE in rice under low N [18]. The outcomes of these experiments have shed light on the enhancement of crop NUE.
Barley is widely used for livestock feed and malting, and a small proportion is consumed as food. Due to its diploid nature, it is a good genetic model for other crops in the Triticeae family. Recent advances in barley NUE research have identified a few QTLs responsible for NUE and related traits [19,20]. However, most are limited by low genetic diversity and the small plant populations used. Indeed, the improvement in NUE in barley is in the early stages and needs further exploration. QTLs controlling NUE and associated genes in the model plant Arabidopsis and other cereal crops are useful for barley NUE research [21,22,23,24]. Therefore, identifying and understanding the genetic basis behind nitrogen use efficiency in barley and then altering the genes through genetic engineering may be a promising approach to improve NUE in barley.

2. Effect of N Fertilizers on Crop Growth and Yield

N plays an important role in the vegetative and reproductive development of crop plants. It is an essential nutrient in almost all stages of the growth cycle of crops for initiating early rapid growth, leaf development, stem extension, and increasing tiller numbers, grain size, grain protein content and, ultimately, yield [25,26]. It is present in the protein structure and chlorophyll, which, in turn, influence photosynthetic activity. High N accelerates the translocation of photosynthetic products from source to sink to increase yield [27]. In rice, yield increased by 16.6% due to an increase in productive tillers under high N supply [28]. The application of high rates of N produces higher yields by increasing major yield components such as tiller number, grain size, and grain number per spike in barley [29,30,31]. On the other hand, yield declines considerably under low N supply. In a study conducted on spring barley, yield declined by 70%–100% under low N compared to high N [29]. Low N stress causes slow growth and chlorosis, where leaf yellowing symptoms occur first in older leaves [26]. N-deficient leaves are narrow, small and erect which might die under severe stress. Eventually, it decreases photosynthesis and in the long-term results in reduced total production of photosynthate and grain yield.
During vegetative growth, plants uptake more N; thus, the shoots and roots incorporate a large quantity of N to increase biomass [32]. In wheat, total biomass, straw biomass and straw N content had a significant positive correlation with yield under N sufficient and deficient conditions [33]. During grain filling, 70%–90% of grain N is transported from internal reserves in vegetative organs [34]. The amount of N that remains in the grain is responsible for grain protein content, which determines grain quality [35,36,37].

3. Nitrogen Uptake, Assimilation and Use Efficiency in Crops

N absorption by plants comprises three main steps: uptake, assimilation and remobilization. N is naturally available from organic matter mineralization, biological N fixation, atmospheric N deposition, irrigation water and other organic sources such as farmyard manure [38]. In addition, inorganic N fertilizers are supplied externally to maximize productivity. Nitrogen is taken up in the form of ammonium or nitrate, depending on the soil conditions, by ammonium (AMT) and nitrate transport (NRT1/NRT2) systems, respectively [39]. Generally, NRT1 is the low-affinity transport system (LATS) and NRT2 is the high-affinity transport system (HATS). Of the NRT1 transporters, AtNRT1.5 is involved in long-distance transport of nitrate from roots to shoots [40]. HATS is active when the external nitrate concentration is low [41]. The upregulated expression of NRT2.1, NRT2.2, NRT2.4 and NRT2.5 in Arabidopsis roots under N starvation is a good example of this [42]. Plant morphology and root characteristics mainly affect N uptake. In general, the root systems in low N soil develop better and extend deeper into the soil to enhance nitrogen uptake [43,44]. Nitrogen uptake also differs at different growth stages. For instance, plants uptake less N during reproductive crop development but facilitate N remobilization [45].
The absorbed inorganic N is converted into organic N compounds through primary and secondary assimilation [46]. Nitrate absorbed is first reduced to nitrite and then to ammonium by nitrate and nitrite reductases, respectively. The ammonium is assimilated in the chloroplast/plastids to amino acids by glutamine synthetase (GS) or glutamate synthase (GOGAT), which are further used for protein synthesis and the catalysis of biological pathways such as photosynthesis [47]. In addition to the GS/GOGAT cycle, some other enzymes including cytosolic asparagine synthetase, carbamoylphosphate synthase (CPSase) and glutamate dehydrogenase (GDH) are involved in ammonium assimilation [39,48]. N remobilization occurs during senescence through extensive degradation of proteins in older leaves to provide N to younger plant organs [39,49]. Studies conducted on Arabidopsis thaliana and Brassica napus revealed that N is remobilized to younger leaves during vegetative growth and seeds during reproductive growth [50,51]. Flag leaf senescence is responsible for N availability for grain filling in barley, wheat and maize [39].
Nitrogen use efficiency (NUE) can be defined in several ways, but the most common definition is grain yield per unit of N supplied (Table 1) [52]. This depends on two major components: Nitrogen Uptake Efficiency (NUpE) and Nitrogen Utilization Efficiency (NUtE) [52,53]. NUpE is the amount of N taken up by the plant per unit of N supplied whereas NUtE is the grain yield per unit of N taken up by the plant. Therefore, NUE is simply the product of NUpE and NUtE [52,54]. NUE is also described as NUEg, which is grain production per unit of N available, or as utilization index (UI), which is the absolute amount of biomass produced per unit of N. Environmental factors affect NUE, which include but are not limited to soil condition, fertilizer types, application timing, and the genotypic variability of the plant [53]. For rainfed wheat in India, topography, rainfall, and moisture availability affected NUE and grain yield [55]. Similar studies have been conducted to check the factors above controlling NUE using a wide range of other crops such as maize, vegetables and root crops [55]. Fertilizer applications and available soil N should be balanced to ensure that N is effectively used. However, more often, N is wasted due to low plant NUE. Thus, improving NUE is essential for cereal crops including barley, to minimize N loss, the negative impacts on the environment, and production costs.

4. NUE Screening and Phenotyping

Preliminary screening of different crop genotypes is necessary to understand their performance under different N concentrations prior to any NUE improvement method. Initially, the yield was considered as the only trait related to NUE, thus stable yield performance under low N supply was a major approach for identifying N-use efficient genotypes. However, various research studies on cereal crops have revealed some other important traits, such as grain protein content, grain nitrogen content, grain weight, and shoot and root parameters (length, dry biomass, etc.) [19,21]. The relative performance of these agronomic traits is generally studied under low and normal N to identify NUE of plants. In rice, deeper roots, longer roots, and higher root length density and root oxidation activity are important traits screened for higher grain yield and NUE under low N conditions [56].
Field experiments are the most commonly used screening method [57], but these are difficult for NUE since they restrict the observation of root characteristics. In fields, N availability should be measured at multiple sites rather than merging a common value for the whole field because N in the soil can vary over very short distances. Therefore, pot and hydroponic experiments in growth chambers have been extensively conducted [12,58]. A comparison of all three screening methods revealed that the latter two approaches reduce environmental interference on genetic screening [29].
Several field experiments have been undertaken to screen barley NUE [29,57,59]. The experimental design (number of plots and replicates), soil N concentration, and geographic and climatic conditions play a key role in field trials [57]. A field trial conducted by [60], using 146 recombinant inbred lines (RILs) from Karl × Lewis in two replicate years identified several significant QTLs for N remobilization across barley chromosomes and several QTLs overlapped with other traits such as N metabolism. Similarly, screening of 224 spring barley accessions at three different locations in two replicate years identified 21 QTLs for thousand kernel weight, which is a major yield component and NUE attribute [61]. A Prisma × Apex barley RIL mapping population was used in pot experiments in two different years, which mapped 41 QTLs for 18 phenotypic traits under low N. Of these, 15 QTLs were responsible for NUE across six chromosomes except for chromosome 4H [20].
However, many studies have suggested that hydroponic experiments overcome the technical difficulties in root phenotyping in N uptake researches [12,62]. Hydroponics, using a nutrient solution as the cultivation medium instead of soil, facilitates the study of the N uptake mechanism and its impact on plant growth [63] with its easy observation of both root and shoot characteristics. Recently, a hydroponic experiment examined the shoot and root traits of five wheat genotypes at four different levels of N to identify high NUE genotypes [12]. Likewise, a hydroponic experiment on 82 Tibetan barley accessions investigated their performance under low N in terms of shoot and root dry biomass [64]. Ideally, performing all three methods together would give the most reliable, precise and comparable results when screening plant NUE.

5. QTL Mapping and the Major Loci Controlling NUE

Nitrogen use efficiency is a quantitative trait controlled by multiple genes [65]. Advances in molecular marker development, quantitative genetics and bioinformatics increase the possibility of identifying quantitative trait loci (QTLs) controlling NUE. QTLs for NUE have been identified in Arabidopsis and other cereals such as rice, wheat and maize [48,66,67,68,69]. Both agronomic traits such as grain yield, grain protein content, and grain weight [66,69,70], and NUE traits such as N remobilization efficiency, N content in the grain and N harvest index [20] have been used as indicators of NUE. In rice, four QTLs have been identified for grain nitrogen content and two QTLs for shoot nitrogen content under both low and normal N on chromosomes 8, 9 and 10 using 166 lines of RILs. In addition, two QTLs were identified on chromosomes 5 and 7 for harvest index and 1 QTL on chromosome 9 for physiological NUE under low N [71]. There are some other QTLs identified in rice for N response, grain yield response and physiological NUE [72]. Recently, significant QTLs have been detected for root NUE, shoot dry weight and grain yield from a wheat TN18×LM6 RIL population [73]. Thus, the studies conducted in rice, wheat and maize set a background for NUE research in barley [21,23,71,74,75].
Although a limited number of studies have been undertaken to identify QTLs controlling NUE under low N in barley, Table 2 summarises a list of major QTLs identified up to date. Fifteen significant QTLs were detected for NUE and its components in the barley Prisma × Apex population under low N [20]. Besides, a few genome-wide association studies have identified QTLs controlling yield, grain weight and grain protein content, which are key indicators of NUE [61,70,76]. However, the results have been inconsistent between studies and between experimental years due to the small mapping populations, low marker density, limited genetic diversity and environmental factors. It seems that QTL mapping to identify candidate genes for NUE is quite challenging. Therefore, it is important to use a large population size with substantial genetic diversity and to conduct multiple field/pot trials across several growing seasons with sufficient biological replicates to minimize these shortcomings and provide more reliable results.

6. Functional Genes for NUE

Genetic and molecular mechanisms in NUE have been extensively investigated in rice and maize, which holds the potential to expand the knowledge to other cereals. As a result, a number of candidate genes and gene families have been identified from these studies to improve NUE [15,65]. Nitrate and ammonium transporters are one of the important functional genes identified. There are about 70 nitrate (NO3−) transporters in Arabidopsis and over 85 in rice that are supposed to be candidates for NUE improvement [48]. Overexpression of OsNRT1.1 in rice under low N conditions in field increased grain yield per plant by 32%–50% and NUE by 38%–54% per plot through a significant increase in seed number per panicle and thousand grain weight whereas its mutations decreased the panicle size, seed setting rate and grain yield [15,80,81]. Similarly, overexpression of OsNRT2.1, OsNRT2.3b and OsPTR9 in rice increased NUE, grain yield and plant growth [18].
The 12 ammonium transporters (AMT) in rice differ in their roles in N uptake and transportation at different growth stages. Transcript levels of most OsAMTs are significantly upregulated in response to low N [82]. For instance, OsAMT1.1 is expressed in both roots and shoots and has an average of a 2.1-fold increase in its expression in response to N deprivation, which enhances ammonium uptake and increases grain yield [83]. Expression of OsAMT1.2 in rice roots increased 8-fold due to N deficiency [82]. Similarly, in Arabidopsis, AtAMT1.1 expression increased approximately 4-fold in response to low N supply [84]. In contrast, the expression of OsAMT1.3 was downregulated in rice roots and produced low grain yields [82]. Hence, the regulation of these transporter genes is strongly correlated with changes in N uptake activity in roots and provides solid evidence for improving NUE in barley.
Many studies suggest that manipulation of genes from primary and secondary N assimilatory pathways is effective for improving NUE [85,86]. For instance, overexpression of glutamine synthetase (GS1) is responsible for primary N assimilation, increased grain yield in rice, wheat and maize [68,87,88]. In maize, knockout of Gln1-3 and Gln1-4 encoding the GS1 enzyme reduced grain yield in gln1-3 and gln1-4 mutants, whereas its overexpression increased yield by increasing kernel number and size [87]. TaGS2-2Ab transgenic lines increased grain yield by 5.4%–11.1% and 8%–13.5% under low N in two consecutive years in wheat. They had longer primary roots and a higher lateral root number than the wild type, which implies high N uptake [89]. Thus, further studies would be helpful to verify these genes as good candidates for improving yield under N deficiency. Correspondingly, glutamate synthase (GOGAT) serves as a potential target for improving NUE. There are two isoforms of GOGAT—the NADH-dependent cytosolic isoform (Iry N assimilation) and ferredoxin-dependent plastidic isoform (IIry N assimilation) [85]. Overexpression of NADH-GOGAT in rice increased spikelet weight and panicle number per plant [90,91]. Fd-GOGAT encoded by ABC1 gene in rice is equally important in N assimilation and carbon/nitrogen balance [92].
Amino acid biosynthesis genes, such as alanine aminotransferase incorporated from barley (HvAlaAT) to rice, increased biomass and grain yield under low N supply [14,93]. Accordingly, yield increased by ~30% in several transgenic rice genotypes tested under ≤50% limited N supply in field conditions [93]. Similarly, metabolite enzyme gene Me1 derived from barley is responsible for NUtE when expressed in wheat [94], suggesting that barley is a good genetic resource for NUE improvement. Overexpression of TaNAC2-5A in wheat increased the tiller and spike number, grain N accumulation, thousand-grain weight under low N compared to high N with ~10% yield increment than the wild type. It also upregulated both the expression of nitrate transporters and assimilation genes [95]. Furthermore, the ARE1 gene in rice is a strong candidate for enhancing NUE. ARE1 mutations delayed senescence and prolonged photosynthesis, which consequently enhanced NUE [16]. When compared with wild-type rice plants, these mutants had a high root to shoot ratio and chlorophyll levels under low N supply [16]. NUE is also indirectly affected by carbon metabolism. Genes involved in N metabolism and nitrate signalling are partially regulated by sugar signalling [86,92]. For instance, overexpression of sugar transporter AtSTP 13 improved N consumption in Arabidopsis [86]. However, further studies should be conducted to better understand the crosstalk of these genes.

7. Candidate Genes for NUE in Barley

The molecular mechanisms and functional characteristics of the genes responsible for NUE in barley have not been determined in detail. However, previous NUE research on cereal crops including rice, wheat, sorghum, maize and the model plant Arabidopsis, has shed some light on the candidate genes in barley through homologous alignment against the reference genome (Table 3). In addition, genes co-localized with QTLs identified in barley (Table 2) may be highly confident for NUE. Of these, nitrate and ammonium transporters, associated partner proteins (NAR2 families), signalling genes, amino acid biosynthesis genes, N assimilation genes and transcriptional factors play key roles in N uptake, transport, assimilation and grain filling [48,65]. Generally, low-affinity transporters (NRT1) are activated at high NO3− levels [96] but in barley, they can be expressed without prior exposure to NO3– and their activity decreases with N accumulation [97]. Recently, the HvNRT2 gene family in barley that encodes high-affinity NO3− transporters were also identified as NUE candidates [19].
A total of 95 candidate genes with potential for NUE improvement across seven chromosomes in the barley genome have been mapped (Table 3; Figure 1): 12 on chromosome 1H, 16 genes each on 2H and 3H, 11 genes on 4H, 13 genes on 5H, 12 on 6H and 15 genes on 7H. They belong to several gene families, viz. ammonium and nitrate transporters, signalling genes, amino acid biosynthesis genes, N assimilation and transcriptional factors. Some gene families, such as nitrate transporters, have been reported for efficient N uptake [48]. The genes are expressed mostly in roots from seedlings (ROO1), roots after 28-day-old plants (ROO2), shoots from seedlings (LEA), senescing leaves (SEN), 4-day-old embryos (EMB), developing tillers on 3rd internode (NOD), etiolated seedlings, dark condition (ETI) and epidermal strips (EPI). Thorough identification of these candidate genes and their expression profile may enable further genetic manipulation for barley NUE improvement.

8. CRISPR/Cas9 Genome Editing for Barley NUE Improvement

Conventional plant breeding is categorized mainly as classical and molecular breeding [100,101]. Classical breeding involves parental crossing to produce improved cultivars by phenotypic analysis over generations. Molecular breeding extends to marker-assisted selection (MAS) and genetic modifications. The newly emerging genome-editing technologies that are correlated with the precise manipulation of an organism’s DNA by the alteration, insertion or deletion of targeted locations in the genome hold a prominent place in plant genomic research. Several approaches have evolved from HR-mediated targeting—from cre-lox editing, zinc finger nucleases (ZFNs) and transcription-like effector nucleases (TALENs) to the most commonly used clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated protein 9 (CRISPR/Cas9) genome editing [102,103,104,105,106]. Compared with ZFNs and TALENs that need expertise in protein engineering, the CRISPR/Cas system needs only two components—Cas9 endonuclease and guide RNA (sgRNA)—which comprise CRISPR RNA and trans-activating CRISPR RNA (crRNA-trcrRNA) transcript. The sgRNA guides the Cas-9 protein, which causes double-strand breaks, to the target site [107]. The CRISPR/Cas system also facilitates multiplex genome editing, high-efficiency targeting and easy customization [105] and is thus more precise, accurate and cost-effective than previous technologies.
The CRISPR/Cas9 system was first used in 2013 in rice and wheat targeting four rice genes and one wheat gene [108]. Recent studies have applied the technology in cereal crops, including wheat, rice, maize, barley and sorghum, to genetically improve yields or nutrient values or to overcome harsh environmental conditions, such as biotic and abiotic stresses [109,110,111,112]. CRISPR/Cas9 was successfully used to target ZmIPK gene in maize to reduce phytic acid contents in maize, and further increase mineral nutrient value [113]. It has also generated new variants of ARGOS8 gene in maize to increase yields under drought stress [114]. Disease resistance in crop plants is another major aspect of CRISPR/Cas9 application, e.g., the development of rice mutant lines to resist blast fungal pathogen by targeting OsERF922 gene [115], wheat mutant lines to induce powdery mildew resistance by targeting TaMLO-A1, TaMLO-B and TaMLO-D genes [111], and a non-transgenic cucumber line, resistant to cucumber vein yellowing disease, papaya ringspot mosaic virus-W and zucchini yellow mosaic virus [116]. In addition, CRISPR/Cas9 was carried out to mutate OsHKT1;4 in rice to study its nutrient use efficiency [117]. CRISPR/Cas9 genome editing was recently used in barley for the first time, targeting HvPM19 to identify its potential for mutation induction and stable transmission, and generated transgene-free plants with the desired mutation [109]. This recent study on barley and other successful applications of CRISPR/Cas9 genome editing are proof for the potential improvement in NUE in barley. To date, most of the genetic studies focussed on overexpression of the genes to improve NUE [95,118]. Hence, the use of CRISPR/Cas9 to downregulate or knockdown genes would be a better approach to improve NUE in barley. For instance, the homolog of rice ARE1 gene [16], which is a promising locus for NUE improvement, might be downregulated to improve nitrogen use efficiency in barley.

9. Conclusions and Perspectives

Excessive use of N fertilizers in crops to boost grain yields is a major cause of soil, water, and air pollution and greenhouse gas emissions. It also has a worldwide economic impact due to the high production costs of N fertilizer. Hence, improving NUE is very important for environmentally friendly, profitable crop production. Genetic improvement of NUE should be a priority to address this issue, although proper management of N fertilizer through agronomic practices is possible. NUE is a polygenic trait that is difficult to quantify. To date, no direct selection criteria have been available for high NUE genotypes other than some agronomic traits, such as root and shoot dry biomass, for conventional breeding.
N fertilization affects the protein content in barley, which is a major concern. Only limited research has been conducted on barley NUE. A few QTLs controlling NUE have been identified, but they are not stable across experiments due to low marker density, limited genetic diversity and small population size. Thus, incorporation of knowledge from other crops such as rice, maize and wheat is desirable to generate a candidate gene pool for NUE improvement. Homologs of these genes can be blast-searched against the genome sequence of barley, and further experiments can be designed to understand the molecular mechanisms of them in barley NUE improvement.

Author Contributions

S.D.K. performed literature search and interpretation of data and drafted the manuscript. Y.H. and X.-Q.Z. provided guidance on relevant literature search and data interpretation. C.L. conceived the project idea. All authors revised the paper and approved the final version to be published.

Funding

This research received no external funding.

Acknowledgments

We would like to acknowledge the expertise assistance from the institution and staff of Western Barley Genetics Alliance (WBGA), Western Australian State Agricultural Biotechnology Centre (SABC), Murdoch University and the Department of Primary Industries and Regional Development, Western Australia. S.D.K. received Murdoch University International Student Scholarship.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Cai, J.; Xia, X.; Chen, H.; Wang, T.; Zhang, H. Decomposition of fertilizer use intensity and its environmental risk in China’s grain production process. Sustainability 2018, 10, 498. [Google Scholar] [CrossRef] [Green Version]
  2. Liu, Y.; Pan, X.; Li, J. A 1961-2010 record of fertilizer use, pesticide application and cereal yields: A review. Agron. Sustain. Dev. 2015, 35, 83–93. [Google Scholar] [CrossRef] [Green Version]
  3. Sharma, L.K.; Bali, S.K. A review of methods to improve nitrogen use efficiency in agriculture. Sustainability 2017, 10, 51. [Google Scholar] [CrossRef] [Green Version]
  4. IFA. Annual Conference, Berlin, “Fertilizer Outlook 2018–2022” PIT and Agriculture Services, IFA. Available online: https://www.fertilizer.org/Public/About_fertilizers/Public/About_Fertilizers/About_Fertilizers.aspx?hkey=c35de5b6-2f79-4db3-93cc-d2cef45ae5d4 (accessed on 5 April 2019).
  5. Chien, S.H.; Teixeirab, L.A.; Cantarellab, H.; Rehmc, G.W.; Grantd, C.A.; Gearhart, M.M. Agronomic effectiveness of granular nitrogen/phosphorus fertilizers containing elemental sulfur with and without ammonium sulfate: A review. Agron. J. 2016, 108, 1203–1213. [Google Scholar] [CrossRef] [Green Version]
  6. Anbessa, Y.; Juskiw, P. Review: Strategies to increase nitrogen use efficiency of spring barley. Can. J. Plant Sci. 2012, 92, 617–625. [Google Scholar] [CrossRef]
  7. Glass, A.D.M. Nitrogen use efficiency of crop plants: Physiological constraints upon nitrogen absorption. CRC Crit. Rev. Plant Sci. 2010, 22, 453–470. [Google Scholar] [CrossRef]
  8. Chen, Z.C.; Ma, J.F. Improving nitrogen use efficiency in rice through enhancing root nitrate uptake mediated by a nitrate transporter, NRT1.1B. J. Genet. Genom. 2015, 42, 463–465. [Google Scholar] [CrossRef]
  9. Ding, W.; Xu, X.; He, P.; Ullah, S.; Zhang, J.; Cui, Z.; Zhou, W. Improving yield and nitrogen use efficiency through alternative fertilization options for rice in China: A meta-analysis. Field Crops Res. 2018, 227, 11–18. [Google Scholar] [CrossRef]
  10. Presterl, T.; Seitz, G.; Landbeck, M.; Thiemt, E.M.; Schmidt, W.; Geiger, H.H. Improving nitrogen use efficiency in European maize. Crop Sci. 2003, 43, 1259–1265. [Google Scholar] [CrossRef]
  11. Wang, R.F.; An, D.G.; Hu, C.S.; Li, L.H.; Zhang, Y.M.; Jia, Y.G.; Tong, Y.P. Relationship between nitrogen uptake and use efficiency of winter wheat grown in North China plain. Crop Pasture Sci. 2011, 62, 504–514. [Google Scholar] [CrossRef]
  12. Ranjitha, K.M.S.; Biradar, S.; Desai, S.A.; Naik, V.R.; Bhat, S.; Satisha, T.N.; Hiremath, G.; Kumar, K.J.Y.; Chethana, C.K.; Venkatesh, K. Media standardization for hydroponic culture to screen wheat genotypes for nitrogen use efficiency. Int. J. Curr. Microbiol. Appl. Sci. 2017, 6, 2814–2820. [Google Scholar] [CrossRef]
  13. Perchlik, M.; Tegeder, M. Improving plant nitrogen use efficiency through alteration of amino acid transport processes. Plant Physiol. 2017, 175, 235–247. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Shrawat, A.K.; Carroll, R.T.; DePauw, M.; Taylor, G.J.; Good, A.G. Genetic engineering of improved nitrogen use efficiency in rice by the tissue-specific expression of alanine aminotransferase. Plant Biotechnol. J. 2008, 6, 722–732. [Google Scholar] [CrossRef] [PubMed]
  15. 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] [PubMed] [Green Version]
  16. 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, 1–10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Good, A.G.; Johnson, S.J.; Pauw, M.D.; Carroll, R.T.; Savidov, N.; Vidmar, J.; Lu, Z.; Taylor, G.; Stroeher, V. Engineering nitrogen use efficiency with alanine aminotransferase. Can. J. Bot. 2017, 85, 252–262. [Google Scholar] [CrossRef]
  18. Huang, S.; Zhao, C.; Zhang, Y.; Wang, C. Nitrogen use efficiency in rice. In Nitrogen in Agriculture-Updates; Amanulla, K., Fahad, S., Eds.; IntechOpen: London, UK, 2017; pp. 187–208. [Google Scholar]
  19. Han, M.; Wong, J.; Su, T.; Beatty, P.H.; Good, A.G. Identification of nitrogen use efficiency genes in barley: Searching for QTLs controlling complex physiological traits. Front. Plant Sci. 2016, 7, 1–7. [Google Scholar] [CrossRef] [Green Version]
  20. Kindu, G.A.; Tang, J.; Yin, X.; Struik, P.C. Quantitative trait locus analysis of nitrogen use efficiency in barley (Hordeum vulgare L.). Euphytica 2014, 199, 207–221. [Google Scholar] [CrossRef]
  21. Li, P.; Chen, F.; Cai, H.; Liu, J.; Pan, Q.; Liu, Z.; Gu, R.; Mi, G.; Zhang, F.; Yuan, L. A genetic relationship between nitrogen use efficiency and seedling root traits in maize as revealed by QTL analysis. J. Exp. Bot. 2015, 66, 3175–3188. [Google Scholar] [CrossRef] [Green Version]
  22. Loudet, O.; Chaillou, S.; Merigout, P.; Talbotec, J.; Daniel-Vedele, F. Quantitative trait loci analysis of nitrogen use efficiency in Arabidopsis. Plant Physiol. 2003, 131, 345–359. [Google Scholar] [CrossRef] [Green Version]
  23. Xu, Y.; Wang, R.; Tong, Y.; Zhao, H.; Xie, Q.; Liu, D.; Zhang, A.; Li, B.; Xu, H.; An, D. Mapping QTLs for yield and nitrogen related traits in wheat: Influence of nitrogen and phosphorus fertilization on QTL expression. Theor. Appl. Genet. 2014, 127, 59–72. [Google Scholar] [CrossRef] [PubMed]
  24. Zhou, Y.; Tao, Y.; Tang, D.; Wang, J.; Zhong, J.; Wang, Y.; Yuan, Q.; Yu, X.; Zhang, Y.; Wang, Y.; et al. Identification of QTL associated with nitrogen uptake and nitrogen use efficiency using high throughput genotyped CSSLs in rice (Oryza sativa L.). Front. Plant Sci. 2003, 8, 1–8. [Google Scholar] [CrossRef] [PubMed]
  25. Ellis, R.P.; Marshall, P. Growth, yield and grain quality of barley (Hordeum vulgare L.) in response to nitrogen uptake: II. Plant development and rate of germination. J. Exp. Bot. 1998, 49, 1021–1029. [Google Scholar] [CrossRef]
  26. Basu, C.P. Nitrogen nutrition in rice. Indian J. Plant Sci. 2015, 4, 28–37. Available online: http://www.cibtech.org/jps.htm (accessed on 27 December 2019).
  27. Narolia, G.P.; Yadav, R.S. Effect of nitrogen levels and its scheduling on growth, yield and grain quality of malt barley (Hordeum vulgare L.) under normal and late sown conditions in North-West Rajasthan. Ann. Arid Zone 2013, 52, 95–99. [Google Scholar]
  28. Liu, X.; Wang, H.; Zhou, J.; Hu, F.; Zhu, D.; Chen, Z.; Liu, Y. Effect of N fertilization pattern on rice yield, nitrogen use efficiency and fertilizer N fate in the Yangtze river basin, China. PLoS ONE 2016, 11, 1–20. [Google Scholar] [CrossRef] [Green Version]
  29. Beatty, P.H.; Anbessa, Y.; Juskiw, P.; Carroll, R.T.; Wang, J.; Good, A.G. Nitrogen use efficiencies of spring barley grown under varying nitrogen conditions in the field and growth chamber. Ann. Bot. 2010, 105, 1171–1182. [Google Scholar] [CrossRef] [Green Version]
  30. Ghoneim, A.M.; Gewaily, E.E.; Osman, M.M.A. Effects of nitrogen levels on growth, yield and nitrogen use efficiency of some newly released Egyptian rice genotypes. Open Agric. 2018, 3, 310–318. [Google Scholar] [CrossRef]
  31. Safina, S.A. Effect of nitrogen levels on grain yield and quality of some barley genotypes grown on sandy soil and salinity irrigation. Egypt J. Agron. 2010, 32, 207–222. Available online: https://www.researchgate.net/publication/279197907 (accessed on 6 January 2020).
  32. Shah, J.M.; Asgher, Z.; Zeng, J.; Quan, X.; Ali, E.; Shamsi, I.H.; Zhang, G. Growth and physiological characterization of low nitrogen responses in Tibetan wild barley (Hordeum spontaneum) and cultivated barley (Hordeum vulgare). J. Plant Nutr. 2016, 40, 861–868. [Google Scholar] [CrossRef]
  33. Gao, S.; Zhang, F.; Zhi, Y.; Chen, F.; Xiao, K. The yields, agronomic, and nitrogen use efficiency traits of wheat cultivars in north China under N-sufficient and deficient conditions. J. Plant Nutr. 2017, 40, 1053–1065. [Google Scholar] [CrossRef]
  34. Yoneyama, T.; Tanno, F.; Tatsumi, J.; Mae, T. Whole plant dynamic system of nitrogen use for vegetative growth and grain filling in rice plants (Oryza sativa L.) as revealed through the production of 350 grains from a germinated seed over 150 days: A review and synthesis. Front. Plant Sci 2016, 7, 1–13. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Janković, S.; Glamočlija, D.; Maletić, R.; Rakić, S.; Hristov, N.; Ikanović, J. Effects of nitrogen fertilization on yield and grain quality in malting barley. Afr. J. Biotechnol. 2011, 10, 19534–19541. [Google Scholar] [CrossRef]
  36. Kılıç, H.; Akar, T.; Kendal, E.; Sayim, I. Evaluation of grain yield and quality of barley varieties under rainfed conditions. Afr. J. Biotechnol. 2010, 9, 7617–7628. Available online: http://www.academicjournals.org/AJB (accessed on 5 January 2020).
  37. Magliano, P.N.; Prystupa, P.; Gutiérrez-Boem, F.H. Protein content of grains of different size fractions in malting barley. J. Inst. Brew. 2014, 120, 347–352. [Google Scholar] [CrossRef]
  38. Gondwe, B.M.; Mweetwa, A.M.; Munyinda, K.; Phiri, E.; Lungu, D. Evaluation of maize genotypes for nitrogen use efficiency. Zambian J. Agric. Sci. 2014, 10, 55–63. Available online: www.researchgate.net/publication/273004258 (accessed on 5 November 2019).
  39. 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] [Green Version]
  40. Lin, S.; Kuo, H.; Canivenc, G.; Lin, C.; Lepetit, M.; Hsu, P.; Tillard, P.; Lin, H.; Wang, Y.; Tsai, C.; et al. Mutation of the Arabidopsis NRT1.5 nitrate transporter causes defective root-to-shoot nitrate transport. Plant Cell 2008, 20, 2514–2528. [Google Scholar] [CrossRef] [Green Version]
  41. Williams, L.E.; Miller, A.J. Transporters responsible for the uptake and partitioning of nitrogenous solutes. Annu. Rev. Plant Physiol. Plant Mol. Biol. 2001, 52, 659–688. [Google Scholar] [CrossRef]
  42. Lezhneva, L.; Kiba, T.; Feria-Bourrellier, A.; Lafouge, F.; Boutete-Mercey, S.; Zoufan, P.; Sakakibara, H.; Daniel-Vedele, F.; Krapp, A. The Arabidopsis nitrate transporter NRT2.5 plays a role in nitrogen acquisition and remobilization in nitrogen-starved plants. Plant J. 2004, 80, 230–241. [Google Scholar] [CrossRef]
  43. Hawkesford, M.J. Reducing the reliance on nitrogen fertilizer for wheat production. J. Cereal Sci. 2014, 59, 276–283. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Noulas, C.; Stamp, P.; Soldati, A.; Liedgens, M. Nitrogen use efficiency of spring wheat genotypes under field and lysimeter conditions. J. Agron. Crop Sci. 2004, 190, 111–118. [Google Scholar] [CrossRef]
  45. Salon, C.; Munier-Jolain, N.G.; Duc, G.; Voisin, A.; Grandgirard, D.; Larmure, A.; Emery, R.J.N.; Ney, B. Grain legume seed filling in relation to nitrogen acquisition: A review and prospects with particular reference to pea. Agronomie 2001, 21, 539–552. [Google Scholar] [CrossRef]
  46. Glass, A.D.M.; Britto, D.T.; Kaiser, B.N.; Kinghorn, J.R.; Kronzucker, H.J.; Kumar, A.; Okamoto, M.; Rawat, S.; Siddiqi, M.Y.E.S.; Joseph, U.; et al. The regulation of nitrate and ammonium transport systems in plants. J. Exp. Bot. 2002, 53, 855–864. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Kumagai, E.; Araki, T.; Hamaoka, N. Ammonia emission from rice leaves in relation to photorespiration and genotypic differences in glutamine synthetase activity. Ann. Bot. 2011, 8, 1381–1386. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. 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]
  49. Have, M.; Marmagne, A.; Chardon, F.; Masclaux-Daubresse, C. Nitrogen remobilization during leaf senescence: Lessons from Arabidopsis to crops. J. Exp. Bot. 2016, 68, 2513–2529. [Google Scholar] [CrossRef]
  50. Diaz, C.; Lemaitre, T.; Christ, A.; Azzopardi, M.; Kato, Y.; Sato, F.; Morot-Gaudry, J.F.; Le-Dily, F.; Masclaux-Daubresse, C. Nitrogen recycling and remobilization are differentially controlled by leaf senescence and development stage in Arabidopsis under low nitrogen nutrition. Plant. Physiol. 2008, 147, 1437–1449. [Google Scholar] [CrossRef] [Green Version]
  51. Malagoli, P.; Laine, P.; Rossato, L.; Ourry, A. Dynamics of nitrogen uptake and mobilization in field-grown winter oilseed rape (Brassica napus) from stem extension to harvest. II.An 15N-labelling-based simulation model of N partitioning between vegetative and reproductive tissues. Ann. Bot. 2005, 95, 1187–1198. [Google Scholar] [CrossRef] [Green Version]
  52. Moll, R.H.; Kamprath, E.J.; Jackson, W.A. Analysis and interpretation of factors which contribute to efficiency of nitrogen utilization. Agron. J. 1982, 74, 562–564. [Google Scholar] [CrossRef]
  53. Anbessa, Y.; Juskiw, P.; Good, A.; Nyachiro, J.; Helm, J. Genetic variability in nitrogen use efficiency of spring barley. Crop. Sci. 2009, 49, 1259–1269. [Google Scholar] [CrossRef]
  54. 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]
  55. Balasubramanian, V.; Alves, B.; Aulakh, M.; Bekunda, M.; ZuCong, C.; Drinkwater, L.; Mugendi, D.; van Kessel, C.; Oenema, O. Crop, environmental and management factors affecting nitrogen use efficiency. In Agriculture and Nitrogen Cycle: Assessing the Impact of Fertilizer Use on Food Production and the Environment; Mosier, A.R., Syers, K.J., Freny, J.R., Eds.; Island Press: Washington, DC, USA, 2004; pp. 19–33. [Google Scholar]
  56. Ju, C.; Buresh, R.J.; Wang, Z.; Zhang, H.; Liu, L.; Yang, J.; Zhang, J. Root and shoot traits for rice varieties with higher grain yield and higher nitrogen use efficiency at lower nitrogen rates application. Field Crops Res. 2015, 175, 47–55. [Google Scholar] [CrossRef]
  57. Swamy, K.N.; Kondamudi, R.; Vijayalakshmi, P.; Jaldhani, V.; Suchandranath, B.M.; Kiran, T.V.; Srikanth, B.; Subhakar, R.I.; Sailaja, N.; Surekha, K.; et al. A comparative study on nitrogen response among Upland, IRHTN, DRR and other released rice groups. Afr. J. Agric. Res. 2015, 10, 4364–4369. [Google Scholar] [CrossRef] [Green Version]
  58. Moose, S.; Below, F.E. Biotechnology approaches to improving maize nitrogen use efficiency. In Molecular Genetic Approaches to Maize Improvement. Biotechnology in Agriculture and Forestry; Kriz, A.L., Larkins, B.A., Eds.; Springer: Berlin/Heidelberg, Germany, 2009; pp. 65–77. [Google Scholar]
  59. Fageria, N.K.; Baligar, V.C. Methodology for evaluation of lowland rice genotypes for nitrogen use efficiency. J. Plant Nutr. 2003, 26, 1315–1333. [Google Scholar] [CrossRef]
  60. Mickelson, S.; See, D.; Meyer, F.D.; Garner, J.P.; Foster, C.R.; Blake, T.K.; Fischer, A.M. Mapping of QTL associated with nitrogen storage and remobilization in barley (Hordeum vulgare L.) leaves. J. Exp. Bot. 2003, 54, 801–812. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  61. Pasam, R.K.; Sharma, R.; Malosetti, M.; Eeuwijk, F.A.V.; Haseneyer, G.; Kilian, B.; Graner, A. Genome-wide association studies for agronomical traits in a worldwide spring barley collection. BMC Plant. Biol. 2012, 12, 1–22. [Google Scholar] [CrossRef] [Green Version]
  62. Garnett, T.; Conn, V.; Kaiser, B.N. Root based approaches to improving nitrogen use efficiency in plants. Plant Cell Environ. 2009, 32, 1272–1283. [Google Scholar] [CrossRef]
  63. An, D.; Su, J.; Liu, Q.; Zhu, Y.; Tong, Y.; Li, J.; Jing, R.; Li, B.; Li, Z. Mapping QTLs for nitrogen uptake in relation to the early growth of wheat (Triticum aestivum L.). Plant. Soil 2006, 284, 73–84. [Google Scholar] [CrossRef]
  64. Yang, L.; Hu, H.; Zhu, B.; Jin, X.; Wu, F.; Zhang, G. Genotypic variations of nitrogen use efficiency in Tibetan wild and cultivated barleys. J. Zhejiang Univ. 2014, 40, 155–164. [Google Scholar] [CrossRef]
  65. Yang, X.; Xia, X.; Zhang, Z.; Nong, B.; Zeng, Y.; Xiong, F.; Wu, Y.; Gao, J.; Deng, G.; Li, D. QTL mapping by whole genome resequencing and analysis of candidate genes for nitrogen use efficiency in rice. Front. Plant Sci. 2017, 8, 1–10. [Google Scholar] [CrossRef] [Green Version]
  66. Agrama, H.A.S.; Zakaria, A.G.; Said, F.B.; Tuinstra, M. Identification of quantitative trait loci for nitrogen use efficiency in maize. Mol. Breed. 1999, 5, 187–195. [Google Scholar] [CrossRef]
  67. Gallais, A.; Hirel, B. An approach to the genetics of nitrogen use efficiency in maize. J. Exp. Bot. 2004, 55, 295–306. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  68. Hirel, B.; Bertin, P.; Quillere’, I.; Bourdoncle, W.; Attagnant, C.I.; Dellay, C.; Gouy, A.I.; Cadiou, S.; Retailliau, C.; Flaque, M.; et al. Towards a better understanding of the genetic and physiological basis for nitrogen use efficiency in maize. Plant Physiol. 2001, 125, 1258–1270. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  69. Ribaut, J.M.; Fracheboud, Y.; Monneveux, P.; Banziger, M. Quantitative trait loci for yield and correlated traits under high and low soil nitrogen conditions in tropical maize. Mol. Breed. 2007, 20, 15–29. [Google Scholar] [CrossRef]
  70. Pauli, D.; Muehlbauer, G.J.; Smith, K.P.; Cooper, B.; Hole, D.; Obert, D.E.; Ullrich, S.E.; Blake, T.K. Association mapping of agronomic QTLs in U.S. spring barley breeding germplasm. Plant Genome 2014, 7, 1–15. [Google Scholar] [CrossRef] [Green Version]
  71. Jiang, W.; Yongbo, D.; Chin, J.H.; Mccouch, S. Identification of QTLs associated with physiological nitrogen use efficiency in rice. Mol. Cells 2007, 3, 72–79. Available online: https://www.researchgate.net/publication/6365528 (accessed on 6 January 2020).
  72. Ye, G.; Huang, J.; Pan, J.; Nie, L. QTL mapping for nitrogen use efficiency and nitrogen deficiency tolerance traits in rice. Plant Soil 2012, 359, 281–295. [Google Scholar] [CrossRef]
  73. Zhang, M.; Gao, M.; Zheng, H.; Yuan, Y.; Zhou, X.; Guo, Y.; Zhang, G.; Zhao, Y.; Kong, F.; An, Y.; et al. QTL mapping for nitrogen use efficiency and agronomic traits at the seedling and maturity stages in wheat. Mol. Breed. 2019, 39, 1–17. [Google Scholar] [CrossRef]
  74. Lei, L.; Li, G.; Zhang, H.; Powers, C.; Fang, T.; Chen, Y.; Wang, S.; Zhu, X.; Carver, B.F.; Yan, L. Nitrogen use efficiency is regulated by interacting proteins relevant to development in wheat. Plant. Biotech. J. 2017, 16, 1214–1226. [Google Scholar] [CrossRef] [Green Version]
  75. Mandolino, C.I.; D’Andrea, K.E.; Olmos, S.E.; Otegui, M.E.; Eyherabide, G.H. Maize nitrogen use efficiency: QTL mapping in a U.S. Dent×Argentine Caribbean Flint RILs population. Maydica 2018, 63, 1–17. Available online: https://www.researchgate.net/publication/324706705 (accessed on 10 January 2020).
  76. Wang, M.; Jiang, N.; Jia, T.; Leach, L.; Cockram, J.; Comadran, J.; Shaw, P.; Waugh, R.; Luo, Z. Genome-wide association mapping of agronomic and morphologic traits in highly structured populations of barley cultivars. Theor. Appl. Genet. 2012, 124, 233–246. [Google Scholar] [CrossRef]
  77. Mansour, E.; Casas, A.M.; Gracia, M.P.; Molina-Cano, J.L.; Moralejo, M.; Cattivelli, L.; Thomas, W.T.B.; Igartua, E. Quantitative trait loci for agronomic traits in an elite barley population for Mediterranean conditions. Mol. Breed. 2013, 33, 249–265. [Google Scholar] [CrossRef] [Green Version]
  78. Comadran, J.; Russell, J.R.; Booth, A.; Pswarayi, A.; Ceccarelli, S.; Grando, S.; Stanca, A.M.; Pecchioni, N.; Akar, T.; Al-Yassin, A.; et al. Mixed model association scans of multi-environmental trial data reveal major loci controlling yield and yield related traits in Hordeum vulgare in Mediterranean environments. Theor. Appl. Genet. 2011, 122, 1363–1373. [Google Scholar] [CrossRef] [Green Version]
  79. Berger, G.L.; Liu, S.; Hall, M.D.; Brooks, W.S.; Chao, S.; Muehlbauer, G.J.; Baik, B.K.; Steffenson, B.; Griffey, C.A. Marker-trait associations in Virginia Tech winter barley identified using genome-wide mapping. Theor. Appl. Genet. 2013, 126, 693–710. [Google Scholar] [CrossRef] [PubMed]
  80. Zhang, J.; Liu, Y.; 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] [PubMed]
  81. Hu, B.; Jiang, Z.; Wang, W.; Qiu, Y.; Zhang, Z.; Liu, Y.; Li, A.; Gao, X.; Liu, L.; Qian, Y.; et al. Nitrate–NRT1.1B–SPX4 cascade integrates nitrogen and phosphorus signalling networks in plants. Nat. Plants 2019, 5, 401–413. [Google Scholar] [CrossRef]
  82. Li, S.; Li, B.; Shi, W. Expression Patterns of Nine Ammonium Transporters in Rice in Response to N Status. Pedosphere 2012, 22, 860–869. [Google Scholar] [CrossRef]
  83. Bao, A.; Liang, Z.; Zhao, Z.; Cai, H. Overexpressing of OsAMT1-3, a high affinity ammonium transporter gene, modifies rice growth and carbon-nitrogen metabolic status. Int. J. Mol. Sci. 2015, 16, 9037–9063. [Google Scholar] [CrossRef] [Green Version]
  84. Shelden, M.; Dong, B.; de Bruxelles, G.L.; Trevaskis, B.; Whelan, J.; Ryan, P.R.; Howitt, S.M.; Udvardi, M.K. Arabidopsis ammonium transporters, AtAMT1;1 and AtAMT1;2, have different biochemical properties and functional roles. Plant. Soil 2001, 231, 151–160. [Google Scholar] [CrossRef]
  85. Pathak, R.R.; Ahmad, A.; Lochab, S.; Raghuram, N. Molecular physiology of plant nitrogen use efficiency and biotechnological options for its enhancement. Curr. Sci. 2008, 94, 1394–1403. Available online: https://www.researchgate.net/publication/216085652 (accessed on 3 January 2020).
  86. Pathak, R.R.; Lochab, S.; Raghuram, N. Plant systems: Improving plant nitrogen-use efficiency. In Comprehensive Biotechnology; Moo-Young, M., Ed.; Elsevier: Amsterdam, The Netherlands, 2011; pp. 209–218. [Google Scholar]
  87. Martin, A.; Lee, J.; Kichey, T.; Gerentes, D.; Zivy, M.; Tatout, C.; Dubois, F.; Balliau, T.; Valot, B.; Davanture, M.; et al. Two cytosolic glutamine synthetase isoforms of maize are specifically involved in the control of grain production. Plant Cell 2006, 18, 3252–3274. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  88. Tabuchi, M.; Sugiyama, K.; Ishiyama, K.; Inoue, E.; Sato, T.; Takahashi, H.; Yamaya, T. Severe reduction in growth rate and grain filling of rice mutants lacking OsGS1;1, a cytosolic glutamine synthetase1;1. Plant J. 2005, 42, 641–651. [Google Scholar] [CrossRef] [PubMed]
  89. 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]
  90. Yamaya, T.; Obara, M.; Nakajima, M.; Sasaki, S.; Hayakawa, T.A.; Sato, T. Genetic manipulation and quantitative-trait loci mapping for nitrogen recycling in rice. J. Exp. Bot. 2002, 53, 917–925. [Google Scholar] [CrossRef] [Green Version]
  91. Tamura, W.; Kojima, S.; Toyokawa, A.; Watanabe, H.; Tabuchi-Kobayashi, M.; Hayakawa, T.; Yamaya, T. Disruption of a novel NADH-glutamate synthase2 gene caused marked reduction in spikelet number of rice. Front. Plant Sci. 2011, 2, 1–11. [Google Scholar] [CrossRef] [Green Version]
  92. Yang, X.; Nian, J.; Xie, Q.; Feng, J.; Zhang, F.; Dong, G.; Liang, Y.; Peng, J.; Wang, G.; Qian, Q.; et al. Rice ferredoxin-dependent glutamate synthase regulates nitrogen–carbon metabolomes and is genetically differentiated between japonica and indica subspecies. Mol. Plant 2016, 9, 1520–1534. [Google Scholar] [CrossRef] [Green Version]
  93. Selvaraj, M.G.; Valencia, M.O.; Ogawa, S.; Lu, Y.; Wu, L.; Downs, C.; Skinner, W.; Lu, Z.; Kridl, J.C.; Ishitani, M.; et al. Development and field performance of nitrogen use efficient rice lines for Africa. Plant. Biotechnol. J. 2017, 15, 775–787. [Google Scholar] [CrossRef] [Green Version]
  94. Górny, A.G.; Banaszak, Z.; Ługowska, B.; Ratajczak, D. Inheritance of the efficiency of nitrogen uptake and utilization in winter wheat (Triticum aestivum L.) under diverse nutrition levels. Euphytica 2010, 177, 191–206. [Google Scholar] [CrossRef] [Green Version]
  95. He, X.; Qu, B.; Li, W. 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]
  96. Fan, X.; Feng, H.; Tan, Y.; Xu, Y.; Miao, Q.; Xu, G. A putative 6-transmembrane nitrate transporter OsNRT1.1b plays a key role in rice under low nitrogen. J. Integr. Plant Biol. 2016, 58, 590–599. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  97. Siddiqi, M.Y.; Glass, A.D.M.; Ruth, T.J.A.; Rufty, J.T.W. Studies of the uptake of nitrate in barley. Plant Physiol. 1990, 93, 1426–1432. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  98. Kumar, A.; Sharma, M.; Kumar, S.; Tyagi, P.; Wani, S.H.; Gajula, M.N.V.P.; Singh, K.P. Functional and structural insights in to candidate genes associated with nitrogen and phosphorus nutrition in wheat (Triticum aestivum L.). Int. J. Biol. Macromol. 2018, 118, 76–91. [Google Scholar] [CrossRef]
  99. Xiong, H.; Guo, H.; Zhou, C.; Guo, X.; Xie, Y.; Zhao, L.; Gu, J.; Zhao, S.; Ding, Y.; Liu, L. A combined association mapping and t-test analysis of SNP loci and candidate genes involving in resistance to low nitrogen traits by a wheat mutant population. PLoS ONE 2019, 14, 1–15. [Google Scholar] [CrossRef]
  100. He, J.; Zhao, X.; Laroche, A.; Lu, Z.X.; Liu, H.; Li, Z. Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding. Front. Plant Sci. 2014, 5, 1–8. [Google Scholar] [CrossRef] [Green Version]
  101. Ashkani, S.; Rafii, M.Y.; Shabanimofrad, M.; Miah, G.; Sahebi, M.; Azizi, P.; Tanweer, F.A.; Akhtar, M.S.; Nasehi, A. Molecular breeding strategy and challenges towards improvement of blast disease resistance in rice crop. Front. Plant Sci. 2015, 6, 1–14. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  102. Bortesi, L.; Fischer, R. The CRISPR/Cas9 system for plant genome editing and beyond. Biotechnol. Adv. 2015, 33, 41–52. [Google Scholar] [CrossRef] [PubMed]
  103. Liu, X.; Xie, C.; Si, H.; Yang, J. CRISPR/Cas9-mediated genome editing in plants. Methods 2017, 121–122, 94–102. [Google Scholar] [CrossRef]
  104. Ma, X.; Zhu, Q.; Chen, Y.; Liu, Y. CRISPR/Cas9 platforms for genome editing in plants: Developments and applications. Mol. Plant. 2016, 9, 961–974. [Google Scholar] [CrossRef] [Green Version]
  105. Ran, F.A.; Hsu, P.D.; Wright, J.; Agarwala, V.; Scott, D.A.; Zhang, F. Genome engineering using the CRISPR-Cas9 system. Nat. Protoc. 2013, 8, 2281–2308. [Google Scholar] [CrossRef] [Green Version]
  106. Arora, L.; Narula, A. Gene editing and crop improvement using CRISPR-Cas9 system. Front. Plant Sci. 2017, 8, 1–21. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  107. Long, L.; Guo, D.; Gao, W.; Yang, W.; Hou, L.; Ma, X.; Miao, Y.; Botella, J.R.; Song, C. Optimization of CRISPR/Cas9 genome editing in cotton by improved sgRNA expression. Plant Methods 2018, 14, 1–9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  108. Shan, Q.; Wang, Y.; Li, J.; Zhang, Y.; Chen, K.; Liang, Z.; Zhang, K.; Liu, J.; Xi, J.J.; Qiu, J.L.; et al. Targeted genome modification of crop plants using a CRISPR-Cas system. Nat. Biotechnol. 2013, 31, 686–688. [Google Scholar] [CrossRef] [PubMed]
  109. Lawrenson, T.; Shorinola, O.; Stacey, N.; Li, C.; Ostergaard, L.; Patron, N.; Uauy, C.; Harwood, W. Induction of targeted, heritable mutations in barley and Brassica oleracea using RNA-guided Cas9 nuclease. Genome Biol. 2015, 16, 1–13. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  110. Svitashev, S.; Schwartz, C.; Lenderts, B.; Young, J.K.; Cigan, A.M. Genome editing in maize directed by CRISPR-Cas9 ribonucleoprotein complexes. Nat. Commun. 2016, 7, 1–7. [Google Scholar] [CrossRef]
  111. Wang, Y.; Cheng, X.; Shan, Q.; Zhang, Y.; Liu, J.; Gao, C.; Qiu, J.L. Simultaneous editing of three homeoalleles in hexaploid bread wheat confers heritable resistance to powdery mildew. Nat. Biotechnol. 2014, 32, 947–951. [Google Scholar] [CrossRef]
  112. Zhou, H.; Liu, B.; Weeks, D.P.; Spalding, M.H.; Yang, B. Large chromosomal deletions and heritable small genetic changes induced by CRISPR/Cas9 in rice. Nucleic Acids Res. 2014, 42, 10903–10914. [Google Scholar] [CrossRef]
  113. Liang, Z.; Zhang, K.; Chen, K.; Gao, C. Targeted Mutagenesis in Zea mays Using TALENs and the CRISPR/Cas System. J. Genet. Genom. 2014, 41, 63–68. [Google Scholar] [CrossRef]
  114. Shi, J.; Gao, H.; Wang, H.; Lafitte, H.R.; Archibald, R.L.; Yang, M.; Hakimi, S.M.; Mo, H.; Habben, J.E. ARGOS8 variants generated by CRISPR-Cas9 improve maize grain yield under field drought stress conditions. Plant Biotechnol. J. 2017, 15, 207–216. [Google Scholar] [CrossRef] [Green Version]
  115. Wang, F.; Wang, C.; Liu, P.; Lei, C.; Hao, W.; Gao, Y.; Liu, K.; Zhao, K. Enhanced rice blast resistance by CRISPR/Cas9-targeted mutagenesis of the erf transcription factor gene OsERF922. PLoS ONE 2016, 11, 1–18. [Google Scholar] [CrossRef]
  116. Chandrasekaran, J.; Brumin, M.; Wolf, D.; Leibman, D.; Klap, C.; Pearlsman, M.; Sherman, A.; Arazi, T.; Gal-On, A. Development of broad virus resistance in non-transgenic cucumber using CRISPR/Cas9 technology. Mol. Plant. Pathol. 2016, 17, 1140–1153. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  117. Mohammed, N.A.A. Exploring Rice Genetic Resources to Improve Nutrient Use Efficiency. Ph.D. Thesis, University of York, York, UK, 2018. [Google Scholar]
  118. Hirel, B.; Le Gouis, J.; Ney, B.; Gallais, A. The challenge of improving nitrogen use efficiency in crop plants: Towards a more central role for genetic variability and quantitative genetics within integrated approaches. J. Exp. Bot. 2007, 58, 2369–2387. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Physical positions of the major candidate gene families for NUE on barley chromosomes. The candidate gene families based on their annotation are indicated on the right side of the chromosome (MapChart 2.32: https://www.wur.nl/en/show/Mapchart.htm).
Figure 1. Physical positions of the major candidate gene families for NUE on barley chromosomes. The candidate gene families based on their annotation are indicated on the right side of the chromosome (MapChart 2.32: https://www.wur.nl/en/show/Mapchart.htm).
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Table
Table 1. Definitions for nitrogen use efficiency (NUE) and its components [52].
Table 1. Definitions for nitrogen use efficiency (NUE) and its components [52].
AbbreviationTermDefinition
NUEN Use EfficiencyNUpE × NUtE = Yield/N supplied
NUpEN Uptake EfficiencyNUp/N(soil + fertilizer) = Acquired N/N available
NUtEN Utilization EfficiencyYield/NUp
NUEgN Use Efficiency GrainGrain production/Available N
UIUtilization IndexTotal plant biomass/Total plant N
Table
Table 2. List of major quantitative trait loci (QTLs) related to NUE and NUE-related traits in barley.
Table 2. List of major quantitative trait loci (QTLs) related to NUE and NUE-related traits in barley.
ChrQTLTraitGenes Co-LocalizedPopulationParent with Positive AlleleReference
1HqYldYieldHvIPT1Morex × BarkeBarke[19]
qYldYieldHvIPT1Orria × PlaisantOrria[77]
qGPCGrain protein contentHvCKX5Morex × BarkeBarke[19,61]
qGWGrain weight [76]
2HqYldYieldHvCKX7, HvGDH3Morex × BarkeBarke[19]
qYldYieldHvPKABA7 [70]
qYldYieldHvCKX7Multiple varietiesn/a[78]
qGPCGrain protein contentHvAMT1.2, HvGS3, HvGOX1, HvIPT2, HvGOX2, HvGOGAT2Morex × BarkeBarke[19]
qGPCGrain protein contentHvCIN2, HvAMT1.2
HvNAM-2, HvGOX1		Lewis × KarlLewis[60]
qGPCGrain protein contentHvIPT2, HvGOX2, HvGOGAT2, HvPKABA5, HvAlaAT2-2, HvCIN2Barley CAP spring linesn/a[70]
qNUEgNUE of grains-Apex × PrismaPrisma[20]
qNutEgNUtE of grains-Apex × PrismaPrisma[20]
qNHIN harvest index of grains-Apex × PrismaPrisma[20]
3HqYldYieldHvCKX3Morex × BarkeBarke[19]
qYldYieldHvASP4, HvCKX3 [70]
qNUEbNUE of above- ground biomass-Apex × PrismaPrisma[20]
qNupEbNUpE of grains-Apex × PrismaPrisma[20]
4HqGPCGrain protein contentHvCIN1, HvGS4Morex × BarkeBarke[19]
qGPCGrain protein contentHvCIN1Barley CAP spring linesn/a[70]
qGPCGrain protein contentHvGS4Multiple varietiesn/a[61]
qGWGrain weightHvGS4Morex × BarkeBarke[19]
qGWGrain weightHvGS4615 UK barley genotypes n/a[76]
5HqYldYield Lewis × KarlLewis[60]
qGPCGrain protein contentHvPKABA6, HvFNR2Morex × BarkeBarke[19,70]
Multiple varietiesn/a[61,70]
qNUEbNUE of above- ground biomass-Apex × PrismaPrisma[20]
qNUEgNUE of grains-Apex × PrismaPrisma[20]
6HqYldYield Morex × BarkeBarke[19]
qYldYieldHvNR3, HvASP5Multiple varietiesn/a[79]
Lewis × KarlLewis[60]
qGPCGrain protein contentHvNR1, HvGS1Morex × BarkeBarke[19]
qGPCGrain protein contentHvNAM1Barley CAP spring linesn/a[70]
qGPCGrain protein contentHvNAM1, HvNAR2.1, HvAMT1.1Lewis × KarlLewis[60]
qGHIHarvest index Apex × PrismaPrisma[20]
7HqYldYieldHvNRT2.7, HvLHT2,
HvLHT3		Morex × BarkeBarke[19]
qYldYieldHvLHT2, HvLHT3Multiple varietiesn/a[78]
qYldYieldHvNRT2.7Multiple varietiesn/a[79]
qGNGrain N Morex × BarkeBarke[19]
qNHIN harvest index of grains-Apex × PrismaPrisma[20]
Cytokinin biosynthesis (IPT), Cytokinin oxidase (CKX), Glutamate dehydrogenase NAD(P)H (GDH), Sucrose non-fermenting-1-related (PKABA), Ammonium transporter (AMT), Glutamine synthetase (GS), Glycolate oxidase (GOX), Glutamate synthase (GOGAT), Cell wall invertase (CIN), NAM transcription factor (NAM), Alanine aminotransferase (AlaAT), Aspartate aminotransferase (ASP), Ferredoxin NAD(P)H reductase (FNR), Nitrate reductase (NR), NRT partner protein (NAR), Nitrate transporter (NRT), Lysine histidine transporter (LH), n/a (not available).
Table
Table 3. Chromosome position of the homologous candidate genes controlling NUE in barley from Arabidopsis, rice and wheat.
Table 3. Chromosome position of the homologous candidate genes controlling NUE in barley from Arabidopsis, rice and wheat.
GeneOriginHomolog in BarleyChrStartEndAnnotation
AtNRT1.1ArabidopsisHORVU7Hr1G0716007H395441113395447440Protein NRT1/ PTR FAMILY
AtAMT1;1, AtAMT1;3ArabidopsisHORVU6Hr1G0578706H377828979377831011Ammonium Transporter 1
AtAMT2ArabidopsisHORVU3Hr1G0826103H599755994599757436Ammonium Transporter 2
AtSTP13ArabidopsisHORVU4Hr1G0674504H559754962559760152Sugar Transporter Protein 7
AtNF-YB1-2ArabidopsisHORVU1Hr1G0716201H494246150494250406Nuclear Transcription Factor Y Subunit B
AtAMT1;3ArabidopsisHORVU3Hr1G0653203H497824332497833404ABC Transporter B Family Member 4
OsDEP1RiceHORVU3Hr1G0518003H375950781375954891Grain Length Protein
OsRGA1RiceHORVU7Hr1G0087207H1133273911337421Guanine Nucleotide-Binding Protein Alpha-1 Subunit
OsSAPK1RiceHORVU2Hr1G1102302H719150904719161174Protein Kinase Superfamily Protein
OsSAPK2RiceHORVU2Hr1G0299002H108667788108672779Protein Kinase Superfamily Protein
OsSAPK3RiceHORVU5Hr1G0976305H605102179605108556Protein Kinase Superfamily Protein
OsSAPK4RiceHORVU3Hr1G0826903H600013901600018673Protein Kinase Superfamily Protein
OsSAPK5, OsPAK7RiceHORVU2Hr1G0754702H543955705543960490Protein Kinase Superfamily Protein
OsSAPK6RiceHORVU1Hr1G0553401H405714931405718538Protein Kinase Superfamily Protein
OsSAPK8RiceHORVU4Hr1G0135404H4780445347807197Protein Kinase Superfamily Protein
OsEND93-1 *, OsEND93-3RiceHORVU7Hr1G0208507H2823780328241820Early Nodulin-Related
OsEND93-2RiceHORVU7Hr1G0207607H2808452028085738Early Nodulin-Related
OsAlaAT10-1, OsAlaAT4RiceHORVU1Hr1G0185401H6836506968370382Alanine Aminotransferase 2
OsAlaAT10-2RiceHORVU5Hr1G0147305H5448754854492982Alanine Aminotransferase 2
OsAlaAT3-1RiceHORVU2Hr1G0637402H431241063431250440Alanine Aminotransferase 2
OsAlaAT3-2RiceHORVU2Hr1G0308202H114313381114319007Alanine Aminotransferase 2
OsGGT1, OsGGT3RiceHORVU1Hr1G0702201H488758496488762295Alanine:Glyoxylate Aminotransferase 3
OsGGT2RiceHORVU4Hr1G0753604H598065082598068656Alanine:Glyoxylate Aminotransferase 2
OsASNase1RiceHORVU2Hr1G0978902H681044647681050401N(4)-(Beta-N-acetylglucosaminyl)-L-Asparaginase
OsASNase2RiceHORVU2Hr1G1230702H754633334754644513Isoaspartyl Peptidase/L-Asparaginase
OsASP2RiceHORVU7Hr1G0892907H541956174541961050Aspartate Aminotransferase 1
OsASP3RiceHORVU6Hr1G0034706H78985347902987Aspartate Aminotransferase 1
OsASP4RiceHORVU3Hr1G0732203H552738455552750250Aspartate Aminotransferase 3
OsASP5RiceHORVU1Hr1G0745901H508562566508569749Aspartate Aminotransferase
OsASP6RiceHORVU1Hr1G0424901H308288850308292215Aspartate Aminotransferase
OsASRiceHORVU5Hr1G0205105H9491380794917732Transcription Initiation Factor TFIID Subunit 8
OsGDH2-3RiceHORVU2Hr1G0930202H656410957656417166Undescribed Protein
OsGDH4RiceHORVU3Hr1G0488703H339064181339071356Glutamate Dehydrogenase
OsGS3RiceHORVU4Hr1G0076104H2017287520175861Glutamine Synthetase 1.3
OsGS4RiceHORVU2Hr1G1113002H722462607722470196Bifunctional Lysine-Specific Demethylase and histidyl-hydroxylase NO66
OsGOGAT1, OsGOGAT3RiceHORVU3Hr1G0630503H482165392482176766Glutamate Synthase 2
OsGOGAT2RiceHORVU2Hr1G0229202H6750316267520099Glutamate Synthase 1
OsGOX2-3RiceHORVU2Hr1G1031802H699321923699325619L-Lactate Dehydrogenase
OsGOX4RiceHORVU2Hr1G0600102H399434162399565758L-Lactate Dehydrogenase
OsGOX5RiceHORVU2Hr1G0309302H115538448115548113L-Lactate Dehydrogenase
OsNR1, OsNR3-4RiceHORVU6Hr1G0033006H76965497701423Nitrate Reductase 1
OsNR2RiceHORVU6Hr1G0797006H538505303538508978Nitrate Reductase 1
OsNiR1-3RiceHORVU6Hr1G0807506H542690954542694406Sulfite Reductase
OsDOF1RiceHORVU7Hr1G0432507H130101918130103443DOF Zinc Finger Protein 1
OsDOF2RiceHORVU4Hr1G0138904H4984395849845261DOF Zinc Finger Protein 1
OsDOF3RiceHORVU5Hr1G0976205H605046251605048334DOF Zinc Finger Protein 1
OsDOF4RiceHORVU6Hr1G0691906H479031099479167490Monodehydroascorbate Reductase 4
OsDOF5RiceHORVU1Hr1G0053901H1168871211691059DOF Zinc Finger Protein 1
OsNF-YB2.1-2.2RiceHORVU3Hr1G0873903H621114774621118012Nuclear Transcription Factor Y Subunit B
OsNF-YB2.3RiceHORVU7Hr1G1054607H617016382617017035Nuclear Transcription Factor Y Subunit B-2
OsHLHm1RiceHORVU4Hr1G0656404H547060963547062633Basic Helix-Loop-Helix (bHLH) DNA-Binding Superfamily Protein
OsHLHm2RiceHORVU4Hr1G0094404H2678835026791410Basic Helix-Loop-Helix (bHLH) DNA-Binding Superfamily Protein
OsHLHm3RiceHORVU3Hr1G0793403H583076029583165960Leucine-Rich Repeat Protein Kinase Family Protein
OsHLHm4RiceHORVU5Hr1G0020905H60367686041581Basic Helix-Loop-Helix (bHLH) DNA-Binding Superfamily Protein
OsNAC006RiceHORVU4Hr1G0120304H3861096438613054NAC Domain Protein
OsNAC6RiceHORVU7Hr1G1064807H619955492619960319NAC Domain Containing Protein 1
OsNAC9/OsSNAC1RiceHORVU5Hr1G1115905H636772198636774461NAC Domain Protein
OsNAC10RiceHORVU5Hr1G0456505H353125420353127305NAC Domain Protein
OsAPO1/OsFBX202RiceHORVU7Hr1G1089707H626595594626597285Aberrant Panicle Organization 1 Protein
OsFBX94RiceHORVU5Hr1G0255305H140302431140306350F-Box Only Protein 13
OsNRT2.3a-2.3bRiceHORVU3Hr1G0660903H503310428503312717High-Affinity Nitrate Transporter 2.6
OsNAR2.1-2.2RiceHORVU5Hr1G1155005H646682607646686179High-Affinity Nitrate Transporter 3.1
OsLHT1RiceHORVU7Hr1G0320607H6559448865596772Lysine Histidine Transporter 2
OsLHT2RiceHORVU7Hr1G0746607H428023559428028502Transmembrane Amino Acid Transporter Family Protein
OsCKX2/Gn1aRiceHORVU3Hr1G0274303H11687986516883601Cytokinin Dehydrogenase 2
OsCKX5RiceHORVU3Hr1G0759203H567046659567052020Cytokinin Dehydrogenase 5
OsCKX4RiceHORVU3Hr1G1053603H668168109668176192Cytokinin Oxidase/Dehydrogenase 1
OsCKX3RiceHORVU1Hr1G0423601H306444595306450221Cytokinin Dehydrogenase 3
OsCKX1RiceHORVU3Hr1G0198503H5840769858410314Cytokinin Oxidase/Dehydrogenase 6
OsCKX7RiceHORVU7Hr1G0867107H522868134522870101Cytokinin Dehydrogenase 10
OsCKX8RiceHORVU1Hr1G0578601H421966219421973332Cytokinin Oxidase/Dehydrogenase 1
OsCKX9RiceHORVU6Hr1G0396806H207624575207626177Cytokinin Oxidase/Dehydrogenase 1
OsIPT1-2RiceHORVU1Hr1G0114801H2782767527830691tRNA Dimethylallyltransferase
OsIPT3RiceHORVU3Hr1G0259503H103350630103351969tRNA Dimethylallyltransferase
OsIPT4-5RiceHORVU5Hr1G1101005H631892524631893928tRNA Dimethylallyltransferase 2
OsCIN1-2RiceHORVU4Hr1G0863004H633598303633602296Beta-Fructofuranosidase, Insoluble Isoenzyme 1
OsCIN3RiceHORVU4Hr1G0110004H3344970033451633Beta-Fructofuranosidase, Insoluble Isoenzyme 3
OsSGR1RiceHORVU5Hr1G0815005H564845582564848348Protein STAY-GREEN Chloroplastic
OsFNR1RiceHORVU2Hr1G0388302H184566812184570474Ferredoxin--NADP Reductase
OsFNR2RiceHORVU5Hr1G1031805H615129595615133117Ferredoxin--NADP Reductase
OsARE1RiceHORVU7Hr1G0637207H314391516314425666Chloroplast envelope membrane protein
TaAS1-3AWheatHORVU3Hr1G0139103H3121214331216892Asparagine synthetase [glutamine-hydrolyzing]
TaASN2-1AWheatHORVU1Hr1G0843701H533821309533827604Asparagine synthetase [glutamine-hydrolyzing] 2
TaASN2-1BWheatHORVU1Hr1G0921101H549769608549775894Asparagine synthetase [glutamine-hydrolyzing] 2
TaANR1-6AWheatHORVU6Hr1G0730406H507069039507080622MADS-box transcription factor 57
TaGS1.1-4AWheatHORVU4Hr1G0668604H555801831555805679Glutamine synthetase 1
TaGDH1-5AWheatHORVU5Hr1G1047005H619890137619895338Glutamate dehydrogenase 1
TaNRT2.1, TaNRT2.4-6AWheatHORVU6Hr1G0056006H1238561512387964High-affinity nitrate transporter 2.6
TraesCS6B01G041800WheatHORVU7Hr1G1200207H650777327650785628Disease resistance protein
TraesCS6B01G043500WheatHORVU6Hr1G0056906H1256585712569544Disease resistance protein
TraesCS6B01G051000WheatHORVU3Hr1G0984503H658650524658656351Receptor kinase 3
TraesCS2A01G128200WheatHORVU0Hr1G002520Un1116095111162387UDP-Glycosyltransferase
TraesCS2A01G127800WheatHORVU2Hr1G1242102H757856039758101641Glutathione-regulated
TraesCS2A01G128400WheatHORVU2Hr1G0224502H6522504765230215Chromodomain-helicase-DNA-binding
TraesCS6B01G194500WheatHORVU6Hr1G0338506H156256740156263950Chaperone protein DnaJ
TraesCS2A01G130100LCWheatHORVU7Hr1G1025007H611628889611629721Phosphoinositide phospholipase C
TraesCS6B01G050700WheatHORVU6Hr1G0068806H1432800114332255Carboxypeptidase Y homolog A
This list of candidate genes is based on several recent reviews from which the homologous genes in barley were identified [48,60,61,78,98,99]. The gene sequences of rice and wheat which were BLAST-searched against barley can be downloaded from http://rice.plantbiology.msu.edu/analyses_search_locus.shtml) and https://plants.ensembl.org/Triticum_aestivum/Info/Index, respectively. Gene IDs and their positions on the barley reference genome and other relevant information are available from IPK Barley BLAST Server and Ensembl Plants using default BLAST parameter settings (https://apex.ipkgatersleben.de/apex/f?p=284:10, http://webblast.ipkgatersleben.de/barley_ibsc/, https://plants.ensembl.org/Hordeum_vulgare/Tools/Blast?db=core).

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Karunarathne, S.D.; Han, Y.; Zhang, X.-Q.; Li, C. Advances in Understanding the Molecular Mechanisms and Potential Genetic Improvement for Nitrogen Use Efficiency in Barley. Agronomy 2020, 10, 662. https://doi.org/10.3390/agronomy10050662

AMA Style

Karunarathne SD, Han Y, Zhang X-Q, Li C. Advances in Understanding the Molecular Mechanisms and Potential Genetic Improvement for Nitrogen Use Efficiency in Barley. Agronomy. 2020; 10(5):662. https://doi.org/10.3390/agronomy10050662

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Karunarathne, Sakura D., Yong Han, Xiao-Qi Zhang, and Chengdao Li. 2020. "Advances in Understanding the Molecular Mechanisms and Potential Genetic Improvement for Nitrogen Use Efficiency in Barley" Agronomy 10, no. 5: 662. https://doi.org/10.3390/agronomy10050662

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