This document discusses genetic engineering strategies for improving nitrogen fixation and nutrient uptake in plants. It describes engineering symbiotic nitrogen fixation by optimizing colonization and carbon/nitrogen exchange between microsymbionts and plant cells. It also discusses improving phosphorus and nitrate uptake through modifying transporter genes and developing phosphite-based fertilization systems. Genome-wide association studies identified genes related to phosphorus efficiency in soybean, including one gene GmACP1 that explained 41% of phenotypic variation. Overall, the document outlines various genetic engineering approaches for enhancing nutrient acquisition in crops.
Genetic engineering for nitrogen fixation and nutrient uptake
1. 12/1/2019 1Dept. of Plant Biotechnology
Genetic Engineering for Nitrogen fixation
and Uptake of Nutrients
Antre Suresh H.
1st Year Ph. D.
PALB 8086
Plant Biotechnology
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Plants assimilate mineral nutrients mainly
as cations or anions
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How much is the right amount of
fertilizer to apply to a field?
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Several factors must be taken into account to engineer a synthetic nitrogen-fixing symbiosis
Engineering of respiratory protection and O2-binding proteins to
allow aerobic nitrogen fixation by microsymbionts
Engineering of synthetic nif clusters
Conditional suppression of ammonium assimilation by
microsymbionts to ensure nitrogen delivery to plants
Optimization of the colonization process
Ensured effective uptake of ammonium by plant cells
Optimization of carbon supply from root cells to
endosymbiotic bacteria
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Fig. Transporters and regulatory elements involved in nitrate metabolism
Nitrogen uptake, transport, and assimilation in plants
Arredondo et al., 2016
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Fig. Phosphorus-scavenging, uptake and assimilation processes targeted by GE strategies
Arredondo et al., 2016
Phosphorus
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Fig. Phosphite oxydoreductase / phosphite-based fertilization and weed control system
Arredondo et al., 2016
GE plant
Phosphite
oxidoreductase
Phosphite oxydoreductase/ phosphite-based fertilization and weed control system
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Asian cultivated rice (Oryza sativa L.) consists of two main subspecies, indica and japonica.
Indica has higher nitrate-absorption activity than japonica.
Variation in a nitrate-transporter gene, NRT1.1B (OsNPF6.5), may contribute to this
divergence in nitrate use.
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Phylogenetic analysis revealed that NRT1.1B diverges between indica and japonica.
NRT1.1B-indica variation was associated with enhanced nitrate uptake and root-to-
shoot transport and up regulated expression of nitrate-responsive genes.
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Putative nitrogen-fixing plant
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Combining linkage analysis, genome-wide and candidate-gene association analyses, and
plant transformation.
Identified a soybean gene related to P efficiency, determined its favorable haplotypes
and developed valuable functional markers.
A highly significant region located on chromosome 8, qPE8, was identified by both GWAs
and linkage mapping and explained 41% of the phenotypic variation.
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Fine mapping and positional cloning of GmACP1
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qPCR of GmACP1 accessions with diverse
phosphorus (P) efficiencies.
Six major genomic regions associated with P efficiency were detected by performing
genome-wide associations (GWAs) in various environments.
The discovery of the optimal haplotype of GmACP1 will now enable the accurate
selection of soybeans with higher P efficiencies and improve our understanding of the
molecular mechanisms underlying P efficiency in plants.
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Genetic strategies for improving macronutrient uptake in some plant species
Blancheteau et al., 2017
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Genetic strategies for improving macronutrient uptake in some plant species
Blancheteau et al., 2017
Editor's Notes
Excessive application of fertilizers not only increases food production costs but also seriously pollute the atmosphere and water bodies, such as rivers and oceans, leading to eutrophication and ocean-dead zones. Hence, the rational use of these resources is imperative to enhance crop yields through
improvement of the N and P uptake and utilization efficiency. In other words, the challenge consists not only in the development of genotypes that use N and P more efficiently, but also in their implementation in better-designed agricultural schemes.
Fig. 1. Transporters and regulatory elements involved in nitrate metabolism. (A) Nitrate is taken up through the root system and allocated to different tissues by the concerted action of numerous transporters. NRT1.1 and NRT2.1 also sense the nitrate status in the environment and inside the plant.
(B) Schematic representation of the signaling pathways that regulate the expression of nitrate-responsive genes.
(C) Schematic representation of the signaling pathways specifically involved in changes in root architecture in response to nitrate.
Fig. 2. Schematic representation of the phosphorus-scavenging, uptake and assimilation processes targeted by genetic engineering strategies. Proteins involved in phosphate scavenging, transport, or regulatory mechanisms and genetically manipulated for enhancing Pi uptake and release from insoluble compounds.
Fig. 3. Phosphite oxydoreductase/ phosphite-based fertilization and weed control system.
Schematic representation of the performance of a phosphite (Phi) fertilization scheme in comparison to a conventional (phosphate) (Pi) fertilization without use of herbicides. In the conventional fertilization, approximately 70% to 80% of the applied Pi gets fixed by adsorption or is converted by soil microorganisms into organic compounds not readily available for plant uptake. Additionally, weed growth is promoted by Pi fertilization. As weeds and most soil microorganisms are unable to use Phi as a phosphorus (P) source, in the Phi fertilization scheme genetically modified plants expressing a phosphite oxidoreductase are more competitive than weeds and soil microorganisms because they can use Phi as sole P source, as illustrated in an increased grain yield. The Phi fertilization system reduces the amount of P fertilizers needed for optimal productivity and also limits the need of herbicides for weed control.
Figure 5 NRT1.1B-indica introgression improves NUE. (a) Gross morphology of Nipponbare (Nip) and the NIL grown in hydroponic solution with varying amounts of nitrate (400 μM, 1 mM and 2 mM) for 3 months after germination. Scale bars, 20 cm. (b) Gross morphology of Nip and the NIL grown in the field (Beijing) under low N (LN) or high N (HN). Scale bars, 20 cm. (c) Total grains per plant of Nip and the NIL grown in the field (Beijing) under LN or HN. Scale bars, 6 cm. (d) Tiller number per plant, grain yield per plant, actual yield per plot and NUE of Nip and the NIL under LN in Beijing. Values are mean ± s.d. (30 replicates for tiller number per plant and grain yield per plant; 6 replicates for actual yield per plot and NUE). P values were generated using Student’s t-test. Nitrate fertilizer was used as the major N source for field cultivation with 1 kg N per 100 m2 for the LN condition and 2 kg N per 100 m2 for the HN condition.
Figure 4 Phylogenetic analysis of NRT1.1B. (a) Phylogram of NRT1.1B generated from 950 diverse rice accessions (four main rice subspecies: indica, japonica, aus and the intermediate type) showing the divergence between indica and japonica. (b) Ancestral reconstruction of the NRT1.1B SNP1 allele. Left, phylogeny of NRT1.1B in the Oryza genus. Right, genotypes of NRT1.1B orthologs in the Oryza genus. Nodes with bootstrap values from 1,000 pseudoreplicates with 45% occurrence or higher are shown. (c) Single-nucleotide diversity and representative genotypes of SNP1 in the indica, japonica and O. rufipogon populations. PSA, population-specific allele.
Efforts are under way to introduce bacterial nif genes into plants, thereby enabling them to fix nitrogen from the air. The use of such plants as crops would reduce the need for fertilizer. ADP, adenosine diphosphate; Pi, inorganic phosphate
Figure 2. Fine mapping and positional cloning of GmACP1.
A phosphorus efficiency quantitative trait locus (QTL) qPE8 was mapped to the interval between the markers Satt089 and Sat_310 on soybean chromosome 8 using 152 recombinant inbred lines (RILs).
(B) This QTL was further delimited to an approximately 250 kb region on chromosome 8 using a natural population composed of the 192 accessions.
(C) The arrow indicates the predicted gene between the markers Sat_233 and BARC-039899-07603, including the five candidate genes Glyma08g20700, Calcineurin B; Glyma08g20710, Phospholipase D; Glyma08g20800 and Glyma08g20820, Putative Phosphatase; and Glyma08g20830, Protein Phosphatase.
(D) The GmACP1 gene model showing the allelic variation (frequency.5%) of the GmACP1 sequence.
(E) Linkage disequilibrium (LD) plot between GmACP1 SNPs.
The physical position of each SNP is shown above the plot. The magnitude of LD indexed by the D9 statistic is also shown. Red squares without numbers
indicate complete LD (D9=1, P,0.01). D9 values are shown in the squares for values ,1.0. Pale blue squares indicate D9 = 1 but inter-marker P$0.01.
doi:10.1371/journal.pgen.1004061.g002
Figure 3. Quantitative real-time PCR of GmACP1 in ten representative accessions with diverse phosphorus (P) efficiencies. The Y-axis denotes the ratio of GmACP1 expression under low P (2P) and high P (+P, control) conditions after seven days.
Figure 5. Phenotypes of hairy roots overexpressing GmACP1 and control hairy roots (CK) cultured by hydroponics and suppliedbwith 0.5 mM phytate.
In situ staining for the activity of acid phosphatase at day 7. The yellow color indicates the enzyme activity in roots. The favorable alleles and haplotypes of GmACP1 associated with increased transcript expression correlated with higher enzyme activity.
(B) The PCR of hairy root DNA using the primers (35S-F+GmACP1-R) to amplify a 1,343-bp fragment. M, Marker; CK, soybean hairy roots transformed with
the control vector pMDC83; 1 to 7, individual plants transformed with the binary vector GmACP1-pMDC83.
(C–F) The effects of hairy roots overexpressing GmACP1 on soybean root dry weight, P concentration and root P use efficiency (ratio of root dry weight to P concentration). The comparisons were performed using ANOVA. * and ** denote significant differences at P = 0.05 and 0.01, respectively.