Omics for the Improvement of Abiotic, Biotic, and Agronomic Traits in Major Cereal Crops: Applications, Challenges, and Prospects
Abstract
:1. Introduction
2. Cereal Genomics: Evolution from Sparse Genetic Markers to Whole-Genome Sequencing
2.1. Genome Sequencing Technologies
2.2. Types of Genomic Variants: Applications in Genetics and Breeding
2.2.1. Single Nucleotide Polymorphisms
2.2.2. Variants Apart from SNPs
2.3. Genetic Mapping
2.3.1. Genome-Wide Linkage Mapping
2.3.2. Genome-Wide Association Studies
2.4. The Study of Species-Level Variations via Pangenomes
2.5. Challenges and Prospects in Crop Genomics
3. Cereal Transcriptomics
3.1. Transcriptomics Techniques
3.2. Transcriptomics to Study Abiotic Stress Tolerance in Plants
Crop | Tissue | Technique | Abiotic Stress | Reference |
---|---|---|---|---|
Rice | Leaves | RNA-Seq | Drought | [75] |
Rice | Leaves | Microarray | Cold | [76] |
Rice | Leaves and shoot | RNA-Seq | Adaptive and salinity | [72] |
Wheat | Roots | RNA-Seq | Drought | [77] |
Wheat | Crown tissue and leaves | RNA-Seq | Cold and light | [78] |
Wheat | Shoots and roots | Microarray | Salinity | [79] |
Maize | Tassels | RNA-Seq | Drought | [80] |
Maize | Leaves | RNA-Seq | Salinity | [81] |
Maize | Leaves | RNA-Seq | Cold | [73] |
Barley | Leaves and roots | Microarray | Drought | [82] |
Barley | Roots | RNA-Seq | Salinity | [83] |
Sorghum | Seedlings | RNA-Seq | Drought | [74] |
Sorghum | Seedlings | RNA-Seq | Salinity | [84] |
Sorghum | Seedlings | RNA-Seq | Salinity | [85] |
3.3. Application of Transcriptomics for Crop Improvement against Biotic Stress
3.4. Challenges and Prospects in Transcriptomics
4. Cereal Proteomics
4.1. Technical Advances in Proteomics
4.2. Global Proteome Profiling
4.2.1. Gel-Based Approaches
4.2.2. Gel-Free Approaches
4.3. Targeted Proteome Profiling
4.3.1. Gel-Based Proteomics
4.3.2. Affinity and Reactive Chemistry-Based Proteomics
4.3.3. MS-Based Proteomics
4.4. Peptidomics, Phosphoproteomics, and Redox Poteomics
4.5. Bioinformatics in Proteomics
4.6. Challenges and Prospects in Proteomics
5. Cereal Metabolomics
5.1. Overview of Metabolomic Pipeline
5.2. Analytical and Data Processing Techniques in Crop Metabolomics
5.3. Applications of Metabolomics for Crop Improvement
Crop | Stress | Techniques | References |
---|---|---|---|
Abiotic stresses | |||
Rice | Flooding | GC–MS, NMR | [178] |
Rice | Drought | GC–MS | [162] |
Rice | Low temperature | LC–MS/MS | [179] |
Wheat | Drought | UPLC–MS | [167] |
Wheat | Low nitrogen | UPLC–QTOF–MS | [180] |
Maize | Salinity | NMR | [181] |
Maize | Drought | GC–TOF–MS | [166] |
Maize | Low nitrogen | GC–MS | [182] |
Barley | Salinity | LC–MS | [163] |
Barley | Drought | GC–MS | [183] |
Sorghum | Drought | GC–MS | [175] |
Sorghum | Low nitrogen | GC–MS/LC–MS | [184] |
Biotic stresses | |||
Rice | Magnoporthe grisea | NMR, GC/LC–MS/MS | [165] |
Rice | Rhizoctonia solani | GC–MS | [168] |
Wheat | Stagonospora nodorum | GC–MS | [185] |
Maize | Fusarium verticillioides | LC–HRM | [186] |
Barley | Fusarium graminearum | HPLC, LC–HRMS | [169] |
Barley | Fusarium graminearum | LC–MS | [173] |
Sorghum | Burkholderia andropogonis | LC–MS | [174] |
5.4. Challenges and Prospects in Crop Metabolomics
6. Cereal Phenomics
Phenotyping Platform/ Techniques | Utilization | References |
---|---|---|
BreedVision | Tractor-pulled multisensory phenotyping platform with RGB, multispectral, and time-of-flight sensors | [197] |
GROWSCREEN fluoro | Work under controlled conditions for quantification of fluorescence pigments | [198] |
Light curtain analysis | Utilized for leaf area and plant height estimation | [199] |
LEAF-E | Estimates the total leaf growth and rate of development | [193] |
Phenocart | A movable platform in the field used for high-throughput phenotyping | [192] |
Phenopsis | Used to study drought tolerance abilities under control conditions | [200] |
Phenoplant | Used to obtain chlorophyll fluorescence parameters under controlled conditions | [201] |
Phenovator | Used for phenotyping a large number of samples under controlled conditions by providing fluorescence, multispectral, and RGB images | [202] |
Pushcarts | Carts with different sensors used to study plant response to drought, heat, and other stresses; operated by one person | [190] |
Terrestrial laser scanning | Used for measuring plant height and architecture under field conditions | [203] |
TRiP | Used to study circadian changes in plants with a series of images and TrRiP algorithm | [204] |
Unmanned aerial platforms | Multiple sensors can be employed for measuring various traits throughout the field | [205] |
6.1. Plant Phenotyping Platforms
6.2. Imaging Sensors and Analysis
6.2.1. RGB/Visible Imaging
6.2.2. Multispectral Imaging
6.2.3. Hyperspectral Imaging
6.2.4. Thermal Imaging
6.2.5. Fluorescence Imaging
6.2.6. X-ray Computed Tomography
Crop | Phenotyping Platform Sensor or Techniques | Field/ Lab | Abiotic Stresses/ Agronomic Traits | Imaging Sensor | Description | Reference |
---|---|---|---|---|---|---|
Rice | Ground-based platforms | Lab | Salinity | Thermal imaging | Plant growth and transpiration rate was used to predict the salinity responses of plants | [214] |
Rice | Ground-based platforms | Field | Nitrogen content | Hyperspectral imaging | Reflectance information and cumulative temperature data were used in the partial least square method for predicting nitrogen status | [210] |
Rice | Ground-based platforms | Field | Drought stress | RGB imaging | Stay green-related feature were extracted for assessing drought-tolerance ability | [196] |
Wheat | Ground-based platforms | Field | Drought | Passive and active hyperspectral reflectance sensors | Performances of different sensors were evaluated for predicting drought tolerance abilities of genotypes with water stress indices | [208] |
Wheat | Manned helicopter | Field | Water and heat stress | Thermal imaging | Canopy temperature was measured in high-throughput way for avoiding the plot-to-plot variation with handheld infrared thermometers | [212] |
Wheat | Ground-based platforms | Field | Nitrogen content | Hyperspectral imaging | Leaf nitrogen status was measured from spectral information with a calibrated model | [217] |
Maize | Organ/tissue phenotyping | Lab | Drought stress | Hyperspectral imaging | Support vector machine classification method separated the water-stressed genotypes from healthy plants with information from vegetation indices | [218] |
Maize | Unmanned aerial vehicle | Field | Water status in plants | Multispectral and thermal imaging | Crop water stress index was predicted from the multispectral images to decipher the plant water status | [219] |
Maize | Unmanned aerial vehicle | Field | Weeds | RGB imaging | Loss of greenness from maize was used for separating weeds from the plants | [220] |
Barley | Ground-based platforms | Field | Drought | Hyperspectral imaging | Linear ordinal support vector machine model was used to predict the drought responses in the plants | [209] |
Barley | Organ/tissue phenotyping | Lab | Salinity | Thermal imaging | Infrared imaging was used to differentiate salt concentration among the genotypes | [191] |
Barley | Unmanned aerial vehicle | Field | Nitrogen use efficiency | RGB, multispectral, and thermal imaging | UAV’s having RGB, multispectral, and thermal imaging was utilized for nitrogen use efficiency | [221] |
Sorghum | Ground-based platforms | Field | Plant height | RGB, ultrasonic, and LIDAR sensor | A comparison was performed for predicting sorghum height, with the LIDAR sensor performing best | [222] |
Sorghum | Unmanned aerial vehicle | Field | Drought stress | RGB imaging | Plant height, biomass, and leaf area were measured for assessing the drought-tolerant abilities of genotypes | [223] |
Crop | Phenotyping Platform/Sensor/Techniques | Field/Lab | Disease/Pest/ Virus | Imaging Sensor | Description | References |
---|---|---|---|---|---|---|
Rice | Ground and aerial platforms | Field/ Lab | Rice blast | Multispectral imaging | Reflectance values were correlated with the disease severity | [224] |
Rice | Organ/tissue phenotyping | Lab | Alfatoxin | Near-infrared spectroscopy | Partial least regression utilized reflectance information for separating infected and healthy seeds | [225] |
Rice | Unmanned aerial vehicle | Field | Rice sheath blight | RGB and multispectral imaging | Percentage of infected leaves from RGB images and vegetation indices from multispectral imaging aid in the detection of rice sheath blight | [226] |
Wheat | Ground-based platforms | Field | Septoria tritici blotch | Hyperspectral imaging | Spectral reflectance indices derived from hyperspectral imaging aids in detecting the presence and severity of Septoria tritici blotch | [189] |
Wheat | Organ/tissue phenotyping | Lab | Fusarium head blight | Hyperspectral imaging | Fusarium head blight was detected using visible-NIR imaging of wheat grain, and grains were separated using linear discrimination and principal component analysis | [227] |
Wheat | Unmanned aerial vehicle | Field | Yellow rust | Hyperspectral imaging | Deep convolutional neural network utilizing both spectral and spatial resolution provided the best performance for predicting yellow rust | [228] |
Maize | Ground and aerial platforms | Field | Northern leaf blight | RGB imaging | A convolutional neural network was used for classifying the infected leaves | [229] |
Maize | Organ/tissue phenotyping | Lab | Alfatoxin infection | Fluorescence imaging | Discriminant analysis from the imaging data aids in the separation of healthy and affected kernels | [213] |
Maize | Unmanned aerial vehicle | Lab | Tar spot | Multispectral and thermal imaging | Disease-progression curve was analyzed using vegetation indices derived from the images | [230] |
Barley | Ground-based platforms | Field | Powdery mildew | Hyperspectral imaging | Support vector machine was used for early detection of disease symptoms by measuring reflection bands | [231] |
Barley | Ground-based platforms | Field | Blast | Hyperspectral imaging | Spectral angle mapping and spectral unmixing analysis was used to locate the pathogen lesions | [232] |
Barley | Organ/tissue phenotyping | Lab | Rust and powdery mildew | Hyperspectral imaging | A simple volume maximization algorithm was developed for differentiating different infected leaves | [233] |
6.3. Challenges and Prospects in Crop Phenomics
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Crop | Mapping Method | Trait or Gene Studied | Reference |
---|---|---|---|
Rice | GWLM and GWAS | Seed vigor | [29] |
Rice | GWLM and GWAS | Bacterial blight-resistant gene, Xa43(t) | [30] |
Rice | GWLM and GWAS | Grain shape and grain weight | [31] |
Rice | GWAS | Plant architecture | [32] |
Rice | GWAS | Salt tolerance, OsSTL1 and OsSTL2 | [33] |
Wheat | GWLM | Plant height and yield | [34] |
Wheat | GWLM | Grain shape and size | [35] |
Wheat | GWLM | Reduced plant height gene, Rht24 | [36] |
Wheat | GWAS | Floret fertility, assimilate partitioning, and spike morphology traits | [37] |
Wheat | GWAS | Total spikelet number | [38] |
Maize | GWLM | Resistance to northern leaf blight | [39] |
Maize | GWLM and GWAS | Plant and ear height | [40] |
Maize | GWLM and GWAS | Male inflorescence size | [41] |
Maize | GWAS | Lipid biosynthesis | [42] |
Maize | GWAS | Root morphology traits | [43] |
Barley | GWLM | Plant height | [44] |
Barley | GWLM | Awn length | [45] |
Barley | GWAS | Photoperiod response | [46] |
Barley | GWAS | Nitrogen use efficiency | [47] |
Barley | GWAS | Spikelet number and grain yield | [48] |
Sorghum | GWLM | Plant height, node number, panicle length, flag leaf length, and flag leaf width | [49] |
Sorghum | GWLM and GWAS | Grain quality traits | [50] |
Sorghum | GWAS | Plant architecture traits (e.g., tiller number, panicle length, seed number, internode length) | [51] |
Sorghum | GWAS | Kernel composition | [52] |
Sorghum | GWAS | Grain size | [53] |
Platform | Read Length (in bps) | Chemical Reaction | Amplification Method | Read Pair | Overall Error Rate |
---|---|---|---|---|---|
1st generation | |||||
Sanger sequencing | 750 | Chain termination | PCR | Yes | — |
2nd generation | |||||
454 Roche | 400 | Pyrosequencing | Emulsion PCR | Yes | 0.5% |
HiSeq Ilumina | 150–300 (paired end) | Reversible termination | Solid-phase PCR | Yes | 0.2% |
SOLiD | 75 (single-end) or 50 (paired-end) | Sequencing by ligation | Emulsion PCR | Yes | 0.1% |
Ion torrent | 200–400 | Proton detection | Emulsion PCR | Yes | 1% |
3rd generation | |||||
PacBio | 25 kb (single-end) | Real-time sequencing | Real-time single- molecular template Hi-Fi | No | 0.1% |
Oxford Nanopore | 30 kb | Disruption of ionic current flow through nanopores | Not required | No | 3% |
Crop | Tissue | Biotic Stress | Reference |
---|---|---|---|
Rice | Leaves | Magnaporthe oryzae | [90] |
Rice | Leaves | Xanthomonas oryzae pv. oryzae | [91] |
Rice | Leaf sheath | Rhizoctonia solani | [92] |
Wheat | Spikes | Fusarium graminearum | [89] |
Wheat | Seedlings | Puccinia triticina | [93] |
Wheat | Leaves | Puccinia striiformis f. sp. tritici | [94] |
Maize | Leaves | Cercospora zeae-maydis; Cercospora zeina | [95] |
Maize | Leaves | Fusarium graminearum | [96] |
Maize | Leaves | Maize Iranian mosaic virus | [97] |
Barley | Leaves | Ramularia coolo-cygni | [98] |
Barley | Leaves | Blumeria graminis f. sp. hordei | [99] |
Barley | Leaves | Rhynchosporium secalis; Cochliobolus sativus | [100] |
Technique | Application | Advantages |
---|---|---|
2D-PAGE | ● Protein separation ● Expression profiling | ● Information about post-translational modifications (PTM) ● Relatively quantitative |
DIGE | ● Separation of proteins ● Quantitative expression profiling | ● Higher sensitivity as compared to 2D-PAGE ● Less gel-to-gel variability ● Multiplexing |
3D-GE | ● Protein separation ● Quantitative expression profiling | ● Overcome co-migration interferences ● High reproducibility |
ICAT | ● Chemical isotope labelling for quantitative proteomics | ● High sensitivity and reproducibility ● Detects low abundant proteins |
iTRAQ | ● Isobaric tagging of proteins | ● High reproducibility ● Multiplexing ● High throughput |
SILAC | ● Isotopic labelling of cells ● Differential expression studies | ● Simple and straightforward quantitation ● Highly sensitive ● Robust ● Degree of labelling is high |
MuDPIT | ● Identification of protein –protein interactions | ● Large protein complex identification |
Crop | Abiotic/Biotic Stresses | Techniques | References |
---|---|---|---|
Rice | Drought | LC–MS/MS | [120] |
Rice | Bakanae disease | TMT–MS | [121] |
Rice | Bacterial blight | 2DE/MudPIT, MALDI–TOF/MS | [122] |
Wheat | Drought | 2D-PAGE | [123] |
Wheat | Drought | 2DE, MALDI–TOF–TOF–MS | [124] |
Wheat | Yellow rust | nanoLC ESI–MS/MS | [125] |
Wheat | Tan spot | 2D-PAGE | [126] |
Maize | Salinization | iTRAQ, LC–MS/MS | [127] |
Maize | Heavy metal | iTRAQ, LC–MS/MS | [128] |
Maize | Ear rot disease | iTRAQ | [129] |
Maize | Maize rough dwarf disease | LC–MS/MS, TMT labeling | [130] |
Barley | Drought | DIGE and LTQ-Orbitrap | [131] |
Barley | Salinization | 2D-PAGE | [132] |
Barley | Leaf rust | LC–MS/MS | [133] |
Barley | Fusarium head blight | 2D-PAGE, MS | [134] |
Barley | Powdery mildew | LC–MS | [135] |
Sorghum | Heavy metal toxicity | 2D-PAGE | [136] |
Sorghum | Drought | DIGE | [137] |
Sorghum | Downy mildew | 2D-PAGE, MLADI–TOF/MS | [138] |
Technique | Description | Advantages |
---|---|---|
LC–MS | Allows profiling of secondary metabolites, such as alkaloids, flavonoids, and phenylpropanoids, based on their different partitioning coefficients between the mobile phase (solvent) and stationary phase (column) | ● Enables detection of metabolites without prior derivatization ● Useful for both reactive and thermally stable metabolites ● High sensitivity to ionized metabolites ● High mass accuracy allows the identification of unknown compounds ● A larger sample, such as 1–50 mL, can be used |
CE–MS | Detect and separate polar or charged metabolites, such as inorganic ions, organic acids, amino acids, vitamins, nucleotides and nucleosides, thiols, carbohydrates, and peptides, based on their charge and size | ● Allow rapid analyses with higher resolution than in LC ● Allow separation of polar or charged metabolites, which are incompatible with LC and GC ● Can use heterogeneous samples ● Easy sample preparation than in GC and LC ● Low reagent use and low cost ● Less quantity of sample, up to 1 uL can be used |
GC–MS | Allow the simultaneous separation and detection of many volatile, thermally stable compounds and primary metabolites, such as sugars, amino acids, organic acids, and polyamines in complex mixtures | ● High resolution ● High sensitivity to non-polar and volatile metabolites ● Lower cost than LC–MS |
NMR | Record the absorption and re-emission energy of atom nuclei due to differences in an external magnetic field | ● Allow detection of unknown metabolites ● Less biased and lower experimental error than in MS-based methods ● Easy sample preparation than in MS methods ● Excellent compound coverage ● Less destructive sampling ● Highly utilized in untargeted metabolomics profiling |
VS | Measures slight differences in vibrational behavior of organic functional groups and chemical bonding under electromagnetic (EM) radiation | ● Non-destructive method ● Minimal to no sample preparation ● Excellent compound coverage ● Untargeted metabolomic profiling with high accuracy ● High reproducibility |
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Kaur, B.; Sandhu, K.S.; Kamal, R.; Kaur, K.; Singh, J.; Röder, M.S.; Muqaddasi, Q.H. Omics for the Improvement of Abiotic, Biotic, and Agronomic Traits in Major Cereal Crops: Applications, Challenges, and Prospects. Plants 2021, 10, 1989. https://doi.org/10.3390/plants10101989
Kaur B, Sandhu KS, Kamal R, Kaur K, Singh J, Röder MS, Muqaddasi QH. Omics for the Improvement of Abiotic, Biotic, and Agronomic Traits in Major Cereal Crops: Applications, Challenges, and Prospects. Plants. 2021; 10(10):1989. https://doi.org/10.3390/plants10101989
Chicago/Turabian StyleKaur, Balwinder, Karansher S. Sandhu, Roop Kamal, Kawalpreet Kaur, Jagmohan Singh, Marion S. Röder, and Quddoos H. Muqaddasi. 2021. "Omics for the Improvement of Abiotic, Biotic, and Agronomic Traits in Major Cereal Crops: Applications, Challenges, and Prospects" Plants 10, no. 10: 1989. https://doi.org/10.3390/plants10101989