Location: Home >> Detail
Crop Breed Genet Genom. 2025;7(2):e250002. https://doi.org/10.20900/cbgg20250002
,
Fengqun Yu *
Saskatoon Research and Development Centre, Agriculture and Agri-Food Canada, 107 Science Place, Saskatoon, SK, S7N OX2, Canada
* Correspondence: Fengqun Yu.
The increasing demand for food, feed, fuel, and fiber in modern society calls for urgent crop improvement, especially when faced with challenges such as climate change and decreasing arable land. Therefore, there is a constant need for advances in plant breeding. Over the last two decades, high-throughput techniques, such as next-generation sequencing, have given momentum to multiple omics technologies, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, generating an immense amount of data daily. These technologies and advanced bioinformatic tools enhance our understanding of agronomically important traits, including yield, nutrient content, and tolerance to biotic/abiotic stresses. For example, research on nucleotide-binding leucine-rich-repeat (NLR) genes, key players in plant immunity, is driven by high-throughput gene discovery, functional annotation, and synthetic design, accelerating disease resistance breeding. Additionally, high-throughput techniques facilitate the generation of valuable tools like molecular markers, which can be utilized in applications such as marker-assisted selection, quantitative trait locus mapping, genome-wide association study, and genomic selection. As regulations on genetically engineered crops move from process-based approaches toward product-based approaches, omics technologies are expected to play a pivotal role in regulating new crop varieties by assessing substantial equivalence. Embracing the omics era in plant breeding requires a paradigm shift in every aspect of the field, and readiness is essential.
Crops cover 40% of the Earth’s land area and provide essential resources like food, fuel, and fiber to support human society [1]. By 2050, as the global population approaches 9.6 billion, the demand for crops is projected to rise by 100%–110% compared to 2005 levels [2]. This increased demand is further challenged by climate change and a reduction in arable land. Sustainable intensification has been proposed by many conservationists as a solution to increase crop yields per hectare without causing environmental harm [3]. This approach is particularly crucial as, in recent decades, new arable land has predominantly been created through deforestation in biodiverse tropical regions, leading to significant biodiversity losses [4,5]. Addressing these challenges necessitates improvements in a wide range of agronomic traits, such as yield, nutritional content, and resistance to biotic and abiotic stresses. Consequently, advances in plant breeding remain critical, requiring both a deeper understanding of the biochemistry, physiology, biology, and agroecology underlying these traits, as well as the development of novel genetic engineering technologies [6]. Plant breeding is mainly carried out through crossbreeding, mutation breeding, and transgenic breeding in modern agriculture. Traditionally, breeding relied solely on phenotypic selection of plant germplasm with desirable traits from progeny produced via crosses or mutagenesis—a labor-intensive, time-consuming process that lacked insights into the genetic composition of selected plants. In the 1980s, the emergence of molecular biology revolutionized plant breeding by enabling the understanding of the genetic basis of traits. This led to the advent of marker-assisted selection (MAS), which uses DNA-based markers to screen plant germplasm for specific alleles. MAS has significantly improved efficiency and reduced costs by permitting the selection of desirable individuals from smaller populations and has been applied extensively in numerous crops [7]. As genomic information has become increasingly available, MAS has become a cornerstone of plant breeding programs [6]. Genetic engineering has introduced transgenic breeding approaches, allowing for the direct insertion of foreign genes into elite crop varieties to confer desired traits. This method bypasses the barriers of reproductive isolation inherent in crossbreeding and avoids the randomness associated with mutation breeding. Since the introduction of genetically engineered (GE) crops in 1994 [8], transgenic breeding has gained increasing prominence in modern agriculture, especially with the advent of the omics era. Advanced genetic engineering techniques, including genome editing and synthetic biology, hold immense potential to revolutionize crop production and plant breeding due to their unparalleled precision and versatility. Nevertheless, transgenic breeding is complementary to traditional breeding, and utilizing both approaches could synergistically enhance progress in crop improvement [6].
Over the past two decades, the development of novel omics technologies has enabled high-throughput analyses of biological molecules, such as the genome (DNA sequence), epigenome (DNA modifications), transcriptome (RNA transcripts), proteome (proteins), and metabolome (metabolites) [9–12]. These tools have provided a comprehensive understanding of the molecular basis of plant breeding, enabling researchers to explore crop biology in unprecedented detail [6,11]. With advances in data acquisition, processing, and accessibility (e.g., lower costs, increased availability of facilities and expertise), multi-omics approaches have been extensively applied to study traits like yield, growth, and stress responses in economically important crops, including rice (Oryza sativa), wheat (Triticum aestivum), barley (Hordeum vulgare), maize (Zea mays), cotton (Gossypium hirsutum), soybean (Glycine max), millet (Setaria italica), and tomato (Solanum lycopersicum) [10,13–16]. Large-scale omics data have also shed light on crucial topics such as the identification of key genes and events during crop domestication, as well as genetic bottlenecks that limit breeding advancements. This knowledge has greatly enhanced breeding efficiency and strategies employed. For example, breeders are leveraging wild relatives, genome-wide association studies (GWAS), gene editing, and pangenomics to restore lost genetic diversity, enhance disease resistance, and improve yield stability [17–23]. In the context of disease resistance breeding, multi-omics approaches are also driving advancements in the study of nucleotide-binding leucine-rich-repeat (NLR) genes. Additionally, fundamental research into each set of biological molecules, especially DNA and RNA, has led to the development or refinement of essential tools, such as genome sequencing, resequencing, and single-nucleotide polymorphism (SNP) assays, which are now integral to plant breeding programs [24–32]. Moreover, omics technologies are increasingly recognized as valuable tools for evaluating the effects of genetic modifications in new crops, aligning with calls for a shift from process-based to product-based regulatory approaches for crop evaluation [6,33].
Genomics investigates the genes and genomes’ structure, function, and evolution, and the resulting enhanced understanding of genetic variation can significantly boost crop breeding efficiency, thereby enabling genetic enhancements in crop species [9]. Pangenomics extends genomics by examining the genetic diversity within a species, encompassing both core and accessory genomes, providing a more comprehensive view of genetic variation. Further, the study of structural genomics involves examining the polymorphism of genome sequences and the organization of chromosomes. Results lead to the construction of genetic and physical maps to aid in the identification of valuable agronomic traits. Structural genomics depends on DNA-based markers to tag and map genes of interest. Molecular markers generated with genomics approaches, such as genotyping-by-sequencing (GBS), have been widely used in MAS by plant breeders to improve crop quality [30,34]. SNPs are single nucleotide variations in the genome among individuals. Historically, PCR-based techniques for testing SNP molecular markers used methods like amplified fragment length polymorphisms (AFLP), and random amplified polymorphic DNA (RAPD) [35–37], but the emergence of next-generation sequencing (NGS) has made it feasible to identify and utilize a large number of SNPs simultaneously in the omics era. Quantitative trait loci (QTL) mapping and GWAS are two methods for studying multiple or complex traits in crops. Mapping QTLs is a statistical approach that can link the data of complex phenotypes to that of genotypes. DNA-based markers, including AFLPs and SNPs, are often used to localize traits of interest using QTL mapping [24–31,38–41]. While using GWAS, based on SNPs identified in genome sequence data, the correlation between genetic variants and phenotypes can be determined or it can identify variants associated with traits in a population [39,42–44]. GWAS is an indispensable genomics approach for studying complex agronomic traits such as tolerance to abiotic stress [17,42]. A study using GWAS identified 48 QTLs that are linked to maize yield under water and heat stress [45], and another study demonstrated the impact of several abiotic stressors on the oil content in sunflowers [46]. Numerous studies have used GWAS to identify QTLs associated with drought tolerance in sorghum (Sorghum bicolor) [47,48], rice [49], and maize [50]. In addition, the association of structural variants (SVs), which are vital in controlling agronomically essential traits in crops, has been reported in maize [51], soybean [52], and oilseed rape (Brassica napus) [53] using GWAS approach. Genomic selection (GS) expands upon GWAS and QTL mapping by utilizing genome-wide markers to predict breeding values, enabling early selection of superior genotypes without the need for full phenotypic evaluations [54–57]. Unlike MAS, which focuses on major-effect loci, GS captures both major and minor QTLs, improving accuracy in selecting for complex traits such as yield, disease resistance, and stress tolerance [58,59]. As a cutting-edge breeding strategy, GS accelerates genetic gain, enhances breeding efficiency, and is increasingly being integrated into modern crop improvement programs to develop resilient and high-yielding cultivars [54].
Functional genomics involves the study of gene functions related to trait regulation. For this, different biotechnological tools and global experimental approaches have been used to identify, clone, and characterize the functions of genes [9]. Gene identification before the omics era was a tedious process that relied on procedures such as expressed sequence tag (EST), suppression subtractive hybridization (SSH), and cDNA-AFLP sequencing. However, the introduction of NGS and the vast resources and data generated by genomics has greatly reduced the tediousness of these approaches [9]. Mutagenesis is a vital technique to develop new crops with desirable traits and is used to determine gene functions [60,61]. Reverse genetic approaches including Virus-induced gene silencing (VIGS) and RNA interference (RNAi) are often utilized to screen/induce mutations to investigate the functions of genes [10,62–64]. VIGS has been used to identify various mutants that are linked to crop yield, growth, and stress tolerance in rice, wheat, barley, and tomato [10,65]. Some high-throughput genomic technologies such as microarray and Targeted Induced Local Lesions IN Genomes (TILLING) are also used for characterizing overall mutational events in crops for functional analyses [11]. Microarray studies showed that transgene insertion caused fewer changes in gene expression profiles compared with changes induced by mutagenesis [61]. TILLING was initially developed as a functional genomics approach [66], but after its successful application in numerous crops to detect and characterize mutations [60,67–73], it became a powerful tool for crop breeding, serving as an alternative approach to transgenesis [74–79]. To serve functional genomics, genome editing tools such as the transcription activator-like effector nuclease (TALEN) and cluster regularly interspaced short palindromic repeats (CRISPR) systems can be utilized for functional analyses and subsequently crop improvement [80–84]. For example, TALEN and CRISPR/Cas9 technologies were applied on characterized mildew resistance locus in bread wheat (TaMlo) or tomato (SlMlo), respectively, and novel mutants with resistance to powdery mildew were successfully generated [85,86]. Since its introduction in 2011, the CRISPR/Cas system has successfully edited the genomes of many important crops through targeted genome editing [81,82,87–94].
DNA modification-epigenomicsEpigenetics explores partially heritable modifications that occur without alterations to the DNA sequence, such as DNA methylation and post-translational modifications of histones [95,96]. Epigenomics is a recent omics approach that combines epigenetics and genomics to study gene regulation and its role in cellular growth and responses to stress [97]. Various environmental factors can affect epigenomics, and genomic-level technologies can be utilized to study these epigenetic changes during different developmental stages or in response to different environmental stimuli [9]. As a result, the study and application of epigenomics could contribute to crop improvement by elucidating how plants respond to various environmental stresses. The bisulfite sequencing (BS-seq) technique has been utilized in various crops, including tomato, maize, and soybean, to identify the DNA methylation status of the genome [98–101]. The methylation-sensitive amplified polymorphism method, which is used to quantify DNA methylation in the genome, has been applied in wheat and foxtail millet [102,103]. Chromatin immunoprecipitation sequencing (ChIP-Seq) studies both histone proteins and DNA methylation [104,105], which has been utilized to study epigenetic alterations in rice in response to drought [106]. Various epigenomic studies have identified epigenetic changes related to ripening in tomato [107], photosynthesis in maize [108], mantled phenotype in oil palm [109], and drought response in cotton [110].
RNA-transcriptomicsTranscriptomics investigates the transcriptome, which is the complete set of RNA transcripts produced by an organism’s genome. Transcriptome profiling is a promising technique that has emerged to analyze time-dependent gene expression in response to internal or external stimuli [111]. Traditional RNA profiling techniques, such as cDNAs-AFLP and SSH, provide low-resolution data [112], but this has greatly improved with the advent of high-throughput approaches such as microarray, RNA sequencing (RNA-seq), and Serial Analysis of Gene Expression (SAGE) [113,114]. These high-throughput approaches are frequently utilized to examine the variances in expression patterns of genes or regulatory networks at various developmental stages and/or in response to stress. The obtained information can aid in functional analyses and could be used to create molecular markers for linking phenotypes in plant breeding aimed at enhancing crops [11]. For example, numerous transcriptomics studies using microarray or RNA-seq have revealed the differentially expressed genes in soybean, barley, sorghum, maize, rapeseed, foxtail millet, sweet potato, and rice at different developmental stages under various stresses [20,115–127]. Notably, some novel advancements in transcriptomics have brought new insights into this field. Spatially resolved transcriptomics is a technique that allows for the detection of gene expression and its spatial distribution within cells or tissues [128], and in situ RNA-seq aims at profiling RNA in living cells or tissues [129]. Comparative transcriptomics identifies the differential expression profiles across different crops in response to stress, and a total of 16 common stress-responsive genes were identified by comparing transcriptomes of wheat, maize, and rice to that of switchgrass under heat stress [130,131]. Recently, alternative splicing transcriptomics was employed to investigate how splicing factors control abiotic stress responses in key crops such as maize, rice, and sorghum [132,133].
Protein-proteomicsProteomics involves the analysis of the entire set of proteins expressed in an organism, encompassing sequence, structural, functional, and expression aspects. [134,135]. Sequence proteomics identifies amino acid sequences via high-performance liquid chromatography (HPLC), while structural proteomics investigates protein structure using computer-based modeling, crystallization, electron microscopy, nuclear magnetic resonance, and X-ray diffraction [136,137]. High-throughput techniques like X-ray crystallography and Nuclear Magnetic Resonance (NMR) spectroscopy have recently been developed to determine protein structure [138]. Functional proteomics determines protein functions through methods like yeast-two-hybrids and protein microarray profiling. Expression proteomics identifies differentially expressed proteins (DEPs) in response to stress conditions through techniques like Stable Isotope Labeling by Amino acids in Cell culture (SILAC), Isotope-Coded Affinity Tag (ICAT) labeling, and Isobaric Tags for Relative and Absolute Quantification (iTRAQ). Proteins can be separated using techniques like Two-Dimensional Gel Electrophoresis (2-DE), Two-Dimensional Difference Gel Electrophoresis (2D-DIGE), and Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis (SDS-PAGE), and analyzed for molecular mass via Mass Spectrometry (MS), Ion Trap (IT)-MS, Liquid Chromatography (LC), Matrix-Assisted Laser Desorption/Ionization-Time of Flight (MALDI-TOF), Electrospray Ionization (ESI), or Collision-Induced Dissociation (CID) [139–141]. Using functional proteomics approaches, many important proteins, such as reactive oxygen species (ROS) scavengers and molecular chaperones, have been identified in tomato, sunflower, wheat, and sugarcane under various stress conditions [142–145], which depicts their crucial function in the defense response of crops. Quantitative proteomics has employed the iTRAQ technique to discover numerous stress-responsive DEPs in potato and coconut [146,147]. Furthermore, using proteomic techniques such as MALDI-TOF, LC-MS/MS, 2-DE, and SDS-PAGE, numerous studies have been conducted to determine the stress-response pathways in rapeseed, soybean, sugarcane, cotton, and chickpea [145,148–152]. Collectively, these proteomic researches have illustrated the significant role of those identified proteins for relevant crops in response to diverse stress conditions. Advancements in the extraction and separation techniques of protein have improved plant proteomic research at both sample and genome scales [153], but there are still limitations to current techniques. One major limitation is sensitivity as proteomic studies typically can only identify the most abundant proteins [154]. Additionally, a variety of sample-preparation methods must be employed to provide a comprehensive assessment of the proteome [154]. Lastly, interpreting the proteomic differences is challenging due to limited knowledge of the functions of many proteins in a plant cell.
Metabolite-metabolomicsMetabolomics investigates the entire set of metabolites produced by metabolic pathways in a biological system using advanced analytical techniques such as MS and NMR spectroscopy [155,156]. Its importance is particularly evident in plant systems, as plants synthesize more metabolites than animals or microbes. Metabolites form a complex defense system in plants against abiotic stress and pathogens, and numerous studies have identified metabolite changes in response to stress in crops such as rice, wheat, maize, tomato, and soybean through targeted and untargeted metabolomics approaches [157]. Using metabolomics methods such as LC-MS, gas chromatography (GC)-MS, and HPLC coupled with tandem MS, several studies have identified some metabolites in rice in response to pathogens like gall midge biotype 1 (GMB1) pathogen, and rice bacterial blight pathogen (Xanthomonas oryzae pv. oryzae) [158,159]. Using similar approaches in wheat, phenolic and phenylpropanoid metabolites were found to be responsive to biotic stress [160]. Additionally, metabolic fingerprinting and metabolic profiling techniques have identified metabolite changes related to drought, cold, and heat stress in wheat, maize, tomato, and soybean [161–167]. Phenotype is bridged with genotype via metabolome [168], thus metabolomics is often integrated with other omics such as genomics, and proteomics to obtain a more comprehensive understanding of biological processes and phenotypes [169]. Under various abiotic stresses, the integrated omics approach has revealed strong correlations among different omics data in crops, providing novel insights into stress response mechanisms [170,171].
Integrated-panomicsOmics technologies could provide “big data” encompassing the entire set of certain biological molecules, but it is still a sectional view with limited information on the complex behaviors of biosystems. To better understand valuable agronomic traits, especially those which are biologically complex (e.g., yield, nitrogen-use efficiency, drought tolerance, etc.), an integration of different omics approaches is beneficial due to their complementary nature. Besides, integrating multi-omics data can help to reduce false positive results and improve the accuracy of genotype-phenotype predictions [172]. To that end, the idea of panomics was proposed [173], which is a platform that enables the integration of diverse omics datasets for the prediction of complex traits [169,174]. For example, the integration of multi-omics datasets has revealed the epigenetic basis of staged single-cell differentiation in cotton fiber [175]. Datasets obtained with different omics approaches need to be merged and analyzed with special tools [176]. For example, PAINTOMICS is an internet-based tool that offers the visual presentation of integrated transcriptomics and metabolomics datasets on Kyoto Encyclopedia of Genes and Genomes (KEGG) maps [177]. Online software tools for multi-omics analysis have been reviewed by Yang et al. [11]. The integration of panomics with GWAS has also been used to study phenotypic variance in crops, potentially leading to the discovery of new genes and functional pathways that underlie valuable but complex agronomic phenotypes [173]. For example, combining GWAS with metabolomics has emerged as an effective method for analyzing the genetic and biochemical mechanisms in model crops like rice, maize, and tomato [178–180]. In recent years, genome editing technologies (such as the CRISPR/Cas9 system) were proposed to be integrated with panomics for precision breeding [173].
The Role of Omics in Advancing NLR ResearchPlants possess sophisticated immune systems to combat diverse biotic stresses, with NLR proteins playing a pivotal role in pathogen recognition and defense activation [181,182]. NLR genes encode intracellular immune receptors that detect pathogen-derived effectors, initiating robust immune responses through a process termed effector-triggered immunity (ETI). This defense mechanism represents the second layer of plant innate immunity, complementing pattern-triggered immunity (PTI), which responds to conserved pathogen-associated molecular patterns (PAMPs) [183–185]. NLR genes are central to plant immunity and have immense potential as targets for crop improvement [186]. The integration of multi-omics approaches has significantly advanced our understanding of their structure, function, and regulation. By combining high-throughput gene discovery, functional annotation, and synthetic design, these approaches are unlocking the full potential of NLR genes for developing resilient and sustainable crops. As these technologies continue to evolve, they will undoubtedly drive further breakthroughs in plant immunity and agricultural biotechnology.
NLR and disease resistance breedingNLR proteins are modular in nature, consisting of three core domains: an N-terminal domain, a central nucleotide-binding (NB-ARC) domain, and a C-terminal leucine-rich repeat (LRR) domain. The N-terminal domain, often a Toll/interleukin-1 receptor (TIR) or coiled-coil (CC) domain, facilitates downstream signaling. The NB-ARC domain functions as a molecular switch, cycling between inactive (ADP-bound) and active (ATP-bound) states, while the LRR domain mediates effector recognition [183,186–189]. NLR proteins detect pathogen effectors either directly, through physical binding, or indirectly, by sensing effector-mediated modifications of host proteins. For example, the Arabidopsis Resistance to Pseudomonas syringae 2 (RPS2) NLR protein recognizes the bacterial effector Avirulence protein Rpt2 (AvrRpt2) through its interaction with the host protein RPM1-Interacting Protein 4 (RIN4) [190]. Upon activation, NLRs trigger a cascade of immune responses, including ROS production, and transcriptional reprogramming of defense genes, which results in a hypersensitive response (HR) whereby localized cell death restricts pathogen spread.
NLR genes have been widely utilized in plant breeding due to their ability to confer race-specific resistance to pathogens. For instance, the Resistance to Phytophthora infestans-blb1 (Rpi-blb1) gene from wild potato confers resistance to Phytophthora infestans, the causative agent of late blight [191]. Similarly, the Xanthomonas resistance 21 (Xa21) gene in rice provides resistance to bacterial blight caused by Xanthomonas oryzae [192]. Leveraging NLR genes has been instrumental in enhancing crop resilience; however, traditional introgression of NLR genes can be labor-intensive and may result in linkage drag. Advances in genome editing technologies, such as CRISPR/Cas9, have enabled precise manipulation of NLR genes to enhance resistance while minimizing trade-offs [193,194]. In addition to their utility in conferring resistance, stacking multiple NLR genes or engineering synthetic NLRs with broader specificity holds promise for durable and broad-spectrum resistance [195,196]. Understanding the molecular mechanisms underlying NLR function and diversity is critical for their effective utilization in breeding programs.
Omics and NLR researchThe advent of multi-omics approaches, including genomics, transcriptomics, proteomics, and epigenomics, has revolutionized our understanding of NLR genes. These integrative strategies have facilitated high-throughput NLR gene discovery, functional annotation, and elucidation of their regulatory networks.
Genomic studies employing NGS and pan-genome analyses have greatly expanded our knowledge of NLR gene diversity [197–199]. By leveraging comparative genomics, researchers have identified lineage-specific expansions and conserved NLR gene clusters, shedding light on their evolutionary trajectories. For instance, GWAS has pinpointed novel NLR loci associated with disease resistance traits, enabling targeted breeding efforts [200]. Transcriptomic and proteomic studies have been instrumental in characterizing NLR gene expression and activity. RNA-seq analyses under pathogen challenge have revealed dynamic expression patterns of NLR genes, providing insights into their roles in specific defense responses [201]. Proteomic approaches, including co-immunoprecipitation and mass spectrometry, have identified NLR protein complexes and downstream signaling components, further elucidating their mechanisms of action [202]. Epigenomic studies have uncovered regulatory elements that modulate NLR gene expression, such as DNA methylation and histone modifications [203–205]. These findings have highlighted the importance of chromatin accessibility in fine-tuning NLR-mediated defense responses. Multi-omics approaches are also paving the way for the rational design of synthetic NLRs. By integrating structural genomics and computational modeling, researchers have engineered synthetic receptors with enhanced specificity and durability [206]. Directed evolution experiments, coupled with high-throughput screening, are enabling the development of NLR variants with novel resistance capabilities.
In addition to facilitating crop improvement through enhanced understanding and the development of valuable tools, omics methods can also play a regulatory role in plant breeding by assessing the health and environmental impacts of new crop varieties developed through both traditional breeding and genetic engineering.
From Process-based to Product-based Regulation of CropsOver the past four decades, the worldwide safety regulations for GE crops have been crafted and updated to exempt conventionally bred crops [33]; however, differentiation no longer makes sense in light of the advances in genetic engineering technologies. Convincing arguments have been made that the current “process-based” safety regulations, which discriminate against GE crops simply because they are developed by genetic engineering technologies, are scientifically invalid and unfit for their original purpose [6,33,207,208]. Product-based regulation, which focuses on the characteristics of the final product rather than the method used to develop it, offers a more relevant approach. This approach considers the actual genetic composition and traits of the crop, ensuring that safety assessments are based on the potential risks posed by the product itself, rather than the technique used to create it. Although authorities may recognize the importance of considering the biological characteristics of new plant products in a process-based regulatory regime, the safety regulations applied are typically determined by the specific technology used to develop the plant variety. However, emerging genetic engineering technologies have increasingly blurred the distinction between conventionally bred crops and their genetically engineered counterparts, causing governing authorities to struggle with redefining which plants require regulation. “Process-based” regulations assume the safety of conventional breeding due to its long familiarity. When GE crops were commercialized in the 1990s, concerns arose over the potential risks to the environment and health due to the random insertion of transgenes in the genome. The argument critics raised was that only 70 plant-synthesized chemicals were monitored and limited animal testing was insufficient to establish the safety status of GE food [6]. Nowadays, genetic engineering technologies, such as the CRISPR/Cas system and other site-specific nucleases (SSNs), can modify a plant’s phenotype significantly by altering a single nucleotide or inserting/deleting genetic sequences of different sizes [81,82]. Some regulatory authorities, such as the United States Department of Agriculture (USDA), believe that such changes could happen naturally and, as a result, are safe and not subject to regulations. Additionally, many current and proposed regulations assume that genes from closely related species are less risky to use than those from distant relatives [208–210]. However, as the 2016 National Academies of Sciences, Engineering, and Medicine (NASEM) report pointed out, the biological effect of genetic transformation and thus the safety risk to environment or health is relatively independent of its size and extent [6]. All genetic alterations, no matter large or small, are aimed at changing a plant’s phenotype to develop a new variety, and these alterations have no fundamental differences when it comes to potential safety risks. Therefore, a revised regulatory framework is necessary, where the need for safety testing is determined by modern molecular technologies such as omics that assess the overall physical and biological traits of new crop varieties [33]. This product-based regulatory approach would ensure a more scientifically sound and effective governance structure.
Evaluation of Substantial Equivalence with Omics TechnologiesRegulatory authorities worldwide have typically relied on the concept of “substantial equivalence” to ensure that newly developed crop varieties have a similar food composition to existing ones and pose no additional risks. While not mandated by regulators, many academic researchers and commercial entities have adopted omics technologies to evaluate substantial equivalence beyond traditional measures of food composition. Ricroch [211] reviewed 60 omics studies of plants, which mainly used a single omics approach like transcriptomics, proteomics, or metabolomics for evaluation. Since then, there has been an increase in the volume of omics studies, and many have used multiple omics methods demonstrating increasing sophistication in this type of assessment [212]. This tendency also indicates that there is a need to establish integrated and shared networks of omics databases serving the evaluation purpose [6,213,214]. The integration of these omics technologies can offer a comprehensive and non-specific evaluation of numerous plant characteristics, encompassing the levels of mRNA, proteins, and metabolites in the plant of interest. Therefore, it is more effective in detecting changes in a GE crop than the evaluation methods currently suggested by regulatory authorities. Some studies have found almost no changes in the transcriptome of GE plants with an added marker gene [215], but the use of metabolomics methods detected changes in the same plants [216]. Adding a gene for a nonenzymatic protein, like the insecticidal Bt toxin, to a GE plant is predicted to result in few changes in the plant’s metabolism. In contrast, when a gene is added to a plant to specifically alter a certain metabolic pathway, several predicted and unpredicted changes can potentially occur. For instance, Shepherd, et al. [217] observed that when the enzymes responsible for the biosynthesis of either of the two toxic glycoalkaloids (alpha-chaconine and alpha-solanine) were downregulated in GE potatoes, the other compound typically increased; while another two compounds, fucosterol and beta-sitosterol, increased with the down-production of both toxic glycoalkaloids. Several studies employing omics methods have identified variations between GE crops and their conventionally-bred counterparts. Nevertheless, in numerous plant characteristics analyzed, the variability among conventionally-bred varieties is greater than that between GE and non-GE varieties. Besides, the developmental stage of the crop and the environmental conditions also affect the findings [211,218].
To regulate new crops in the future based on the core concept of “product-based regulation” and “substantial equivalence”, the omics features of any new crop variety could be compared to all the current varieties across a country or reference panel to determine whether they are substantially different. In this way, both GE and non-GE crops could be regulated, and the regulation efforts could be concentrated on the omics features of the plants and products from new potential varieties. A tiered regulatory approach has been proposed based on the comparison of omics characteristics of a new crop variety with a panel of existing commercial varieties. This panel could consist of conventionally bred varieties representing the genetic and phenotypic diversity of the species. Depending on the results of the omics evaluation with various omics technologies, four paths could be taken [6,33]. In Tier 1, no differences between the new variety and the panel varieties are detected by any omics technologies. In Tier 2, the differences detected by any omics technologies are known to have no adverse effects on human/animal health or the environment. New varieties evaluated as Tier 1 or Tier 2 crops shall require no further testing. In Tier 3, the differences detected are understood to have potential adverse effects. In Tier 4, the detected differences cannot be interpreted. Thus new varieties evaluated as Tier 3 or Tier 4 require further safety testing. For instance, introducing a previously approved GE trait into a new variety of the same species and comparing its omics profile to another deregulated GE variety already in use establishes substantial equivalence and categorizes the new variety as Tier 1. If omics analyses detect differences that may have potential adverse health effects or changes of a protein or metabolite whose consequences are unknown, they are categorized as Tier 3 and 4, respectively, and require further safety testing. The tiered regulatory approach would provide a structured framework for the use of omics evaluation methods in regulatory decision-making [6].
Ideally, panomics data including all omics data available such as genomics, transcriptomics, proteomics, and metabolomics, should be used for the evaluation of substantial equivalence. To leverage the potential of omics technologies for substantial equivalence assessment, it is crucial to develop comprehensive species-specific omics databases that showcase the transcriptome, proteome, and metabolome variations of diverse genotypes grown under various environmental conditions. Currently, state-of-the-art different omics technologies vary considerably. New developments in DNA/RNA sequencing, such as NGS, have made it possible to acquire complete genomes or transcriptomes at acceptable speed and cost. Therefore, transcriptomics and genomics have the potential to play a crucial role in evaluating substantial equivalence. In contrast, proteomic and metabolomics technologies cannot produce a comprehensive catalog of the proteome or metabolome at present [219,220], but they can still contribute to the evaluation. For instance, if a new crop variety has a proteome or metabolome similar to a de-regulated variety, it may indicate substantial equivalence, whereas a difference suggests the need for further evaluation. Scientists understand that the current technical limitations of omics technologies for plants must be overcome by new research [219,220], and much greater investment especially from the public sector will be needed. To avoid the long-term backlash from the public like the first generation of GE crops, scientists, regulators, and diverse members of the public must work together to guide the research, and they should all be included in the deliberations on those important questions like what differences between new crops and existing ones would be significant enough to warrant safety regulation [6,33]. The most comprehensive evidence of substantial equivalence would involve a complete understanding of the biochemical makeup of a given crop variety in comparison to others, but current technology does not yet allow for the development of extensive species-specific proteome or metabolome databases. Looking into the future, ongoing advances in omics technologies will surely expand basic knowledge of those agronomical traits in plants and thus greatly fuel molecular plant breeding.
At the time of writing the review, a reference genome was available for almost all major crop species. In addition, an immense amount of multi-omics data is being generated and uploaded to online databases daily. With the help of advanced bioinformatic tools, the omics data are being utilized by scientists to improve our understanding of valuable agronomic traits such as disease resistance, stress tolerance, and yield in crops, and ultimately used in plant breeding and crop improvements. Besides, as the regulation regime of GE crops worldwide shifts towards product-based approaches, omics technologies are expected to play a crucial role in the evaluation of substantial equivalence. With all these clear signs showing a bright future for the vast potential of omics technologies, we should all get ready to embrace the omics era for plant breeding. This could mean changes in every aspect of crop science including our understanding of plant traits, tools we use, and even working logic or concepts. However, it is important to view this paradigm shift as complementary to our current work routine, as we still face several challenges in the omics era. First, omics data are incomplete, with varying quality of reference genomes depending on technical and cost limitations. Therefore, multiple high-quality reference genomes are needed to capture diversity within a crop variety adequately. Second, users may not fully utilize omics data due to the lack of access, computational tools, or analytical skills. These limitations can be overcome with the development of omics and bioinformatics technologies. Finally, phenotyping technologies are still limited compared to fast-evolving omics technologies [221], and in the future, field-based high-throughput phenotyping approaches with increased efficiency and reduced cost are likely to be developed to parallel datasets with omics technologies and aid in plant breeding [222,223].
No data were generated from the study.
Conceptualization, HH and FY; Writing—Original Draft Preparation, HH; Writing—Review & Editing, HH and FY; Supervision, FY; Project Administration, HH and FY; Funding Acquisition, HH and FY.
The authors declare that they have no conflict of interest in relation to this work.
This work was supported by Saskatchewan Agriculture Development Fund, SaskCanola, and Western Grains Research Foundation.
We thank Sally Vail at the Saskatoon Research and Development Centre, Agriculture and Agri-Food Canada for her critical review of the manuscript.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.
81.
82.
83.
84.
85.
86.
87.
88.
89.
90.
91.
92.
93.
94.
95.
96.
97.
98.
99.
100.
101.
102.
103.
104.
105.
106.
107.
108.
109.
110.
111.
112.
113.
114.
115.
116.
117.
118.
119.
120.
121.
122.
123.
124.
125.
126.
127.
128.
129.
130.
131.
132.
133.
134.
135.
136.
137.
138.
139.
140.
141.
142.
143.
144.
145.
146.
147.
148.
149.
150.
151.
152.
153.
154.
155.
156.
157.
158.
159.
160.
161.
162.
163.
164.
165.
166.
167.
168.
169.
170.
171.
172.
173.
174.
175.
176.
177.
178.
179.
180.
181.
182.
183.
184.
185.
186.
187.
188.
189.
190.
191.
192.
193.
194.
195.
196.
197.
198.
199.
200.
201.
202.
203.
204.
205.
206.
207.
208.
209.
210.
211.
212.
213.
214.
215.
216.
217.
218.
219.
220.
221.
222.
223.
Hu H, Yu F. Embracing the Omics Era for Plant Breeding. Crop Breed Genet Genom. 2024;7(2):e250002. https://doi.org/10.20900/cbgg20250002
Copyright © Hapres Co., Ltd. Privacy Policy | Terms and Conditions
