Plant Networks as Traits and Hypotheses: Moving Beyond Description

A systems genomics and genetics approach to identify the genetic regulatory network for lignin content in Brassica napus seeds

Seed quality traits of oilseed rape, Brassica napus (B. napus), exhibit quantitative inheritance determined by its genetic makeup and the environment via the mediation of a complex genetic architecture of hundreds to thousands of genes. Thus, instead of single gene analysis, network-based systems genomics and genetics approaches that combine genotype, phenotype, and molecular phenotypes offer a promising alternative to uncover this complex genetic architecture. In the current study, systems genetics approaches were used to explore the genetic regulation of lignin traits in B. napus seeds. Four QTL (qLignin_A09_1, qLignin_A09_2, qLignin_A09_3, and qLignin_C08) distributed on two chromosomes were identified for lignin content. The qLignin_A09_2 and qLignin_C08 loci were homologous QTL from the A and C subgenomes, respectively. Genome-wide gene regulatory network analysis identified eighty-three subnetworks (or modules); and three modules with 910 genes in total, were associated with lignin content, which was confirmed by network QTL analysis. eQTL (expression quantitative trait loci) analysis revealed four cis-eQTL genes including lignin and flavonoid pathway genes, cinnamoyl-CoA-reductase (CCR1), and TRANSPARENT TESTA genes TT4, TT6, TT8, as causal genes. The findings validated the power of systems genetics to identify causal regulatory networks and genes underlying complex traits. Moreover, this information may enable the research community to explore new breeding strategies, such as network selection or gene engineering, to rewire networks to develop climate resilience crops with better seed quality.

SIMPEL: using stable isotopes to elucidate dynamics of context specific metabolism

The capacity to leverage high resolution mass spectrometry (HRMS) with transient isotope labeling experiments is an untapped opportunity to derive insights on context-specific metabolism, that is difficult to assess quantitatively. Tools are needed to comprehensively mine isotopologue information in an automated, high-throughput way without errors. We describe a tool, Stable Isotope-assisted Metabolomics for Pathway Elucidation (SIMPEL), to simplify analysis and interpretation of isotope-enriched HRMS datasets. The efficacy of SIMPEL is demonstrated through examples of central carbon and lipid metabolism. In the first description, a dual-isotope labeling experiment is paired with SIMPEL and isotopically nonstationary metabolic flux analysis (INST-MFA) to resolve fluxes in central metabolism that would be otherwise challenging to quantify. In the second example, SIMPEL was paired with HRMS-based lipidomics data to describe lipid metabolism based on a single labeling experiment. Available as an R package, SIMPEL extends metabolomics analyses to include isotopologue signatures necessary to quantify metabolic flux.

Laminarin-triggered defence responses are geographically dependent in natural populations of Solanum chilense

Natural plant populations are polymorphic and show intraspecific variation in resistance properties against pathogens. The activation of the underlying defence responses can depend on variation in perception of pathogen-associated molecular patterns or elicitors. To dissect such variation, we evaluated the responses induced by laminarin (a glucan, representing an elicitor from oomycetes) in the wild tomato species Solanum chilense and correlated this to observed infection frequencies of Phytophthora infestans. We measured reactive oxygen species burst and levels of diverse phytohormones upon elicitation in 83 plants originating from nine populations. We found high diversity in basal and elicitor-induced levels of each component. Further we generated linear models to explain the observed infection frequency of P. infestans. The effect of individual components differed dependent on the geographical origin of the plants. We found that the resistance in the southern coastal region, but not in the other regions, was directly correlated to ethylene responses and confirmed this positive correlation using ethylene inhibition assays. Our findings reveal high diversity in the strength of defence responses within a species and the involvement of different components with a quantitatively different contribution of individual components to resistance in geographically separated populations of a wild plant species.

Coexpression Network Construction and Visualization from Transcriptomes Underlying ER Stress Responses

Dynamic gene expression changes are primary cellular reactions in response to most stresses and developmental cues in all organisms, including plants. With the ever-decreasing cost and increasing access, high-throughput transcriptome analyses have become a significant research tool to understand a wide spectrum of complex gene regulatory mechanisms. However, it is still challenging to understand the complete picture of gene responses because of the interactive and dynamic nature of gene expression in biological networks. Coexpression network analyses followed by network mapping are being increasingly applied to overcome this challenge. In this chapter, we will introduce detailed instructions for performing a weighted coexpression network analysis (WGCNA) and network visualization using a transcriptome dataset obtained during recovery from endoplasmic reticulum (ER) stress in Arabidopsis thaliana. The streamlined workflow described here allows biologists to identify and visualize coexpression interactions among genes, accessing a comprehensive landscape of dynamic gene expression changes for further downstream analyses using their datasets.

A Practical Guide to Inferring Multi-Omics Networks in Plant Systems

The inference of gene regulatory networks can reveal molecular connections underlying biological processes and improve our understanding of complex biological phenomena in plants. Many previous network studies have inferred networks using only one type of omics data, such as transcriptomics. However, given more recent work applying multi-omics integration in plant biology, such as combining (phospho)proteomics with transcriptomics, it may be advantageous to integrate multiple omics data types into a comprehensive network prediction. Here, we describe a state-of-the-art approach for integrating multi-omics data with gene regulatory network inference to describe signaling pathways and uncover novel regulators. We detail how to download and process transcriptomics and (phospho)proteomics data for network inference, using an example dataset from the plant hormone signaling field. We provide a step-by-step protocol for inference, visualization, and analysis of an integrative multi-omics network using currently available methods. This chapter serves as an accessible guide for novice and intermediate bioinformaticians to analyze their own datasets and reanalyze published work.

Genome-Wide Association Studies across Environmental and Genetic Contexts Reveal Complex Genetic Architecture of Symbiotic Extended Phenotypes

A goal of modern biology is to develop the genotype-phenotype (G→P) map, a predictive understanding of how genomic information generates trait variation that forms the basis of both natural and managed communities. As microbiome research advances, however, it has become clear that many of these traits are symbiotic extended phenotypes, being governed by genetic variation encoded not only by the host's own genome, but also by the genomes of myriad cryptic symbionts. Building a reliable G→P map therefore requires accounting for the multitude of interacting genes and even genomes involved in symbiosis. Here, we use naturally occurring genetic variation in 191 strains of the model microbial symbiont Sinorhizobium meliloti paired with two genotypes of the host Medicago truncatula in four genome-wide association studies (GWAS) to determine the genomic architecture of a key symbiotic extended phenotype-partner quality, or the fitness benefit conferred to a host by a particular symbiont genotype, within and across environmental contexts and host genotypes. We define three novel categories of loci in rhizobium genomes that must be accounted for if we want to build a reliable G→P map of partner quality; namely, (i) loci whose identities depend on the environment, (ii) those that depend on the host genotype with which rhizobia interact, and (iii) universal loci that are likely important in all or most environments. IMPORTANCE Given the rapid rise of research on how microbiomes can be harnessed to improve host health, understanding the contribution of microbial genetic variation to host phenotypic variation is pressing, and will better enable us to predict the evolution of (and select more precisely for) symbiotic extended phenotypes that impact host health. We uncover extensive context-dependency in both the identity and functions of symbiont loci that control host growth, which makes predicting the genes and pathways important for determining symbiotic outcomes under different conditions more challenging. Despite this context-dependency, we also resolve a core set of universal loci that are likely important in all or most environments, and thus, serve as excellent targets both for genetic engineering and future coevolutionary studies of symbiosis.

Temporal and spatial auxin responsive networks in maize primary roots

Auxin is a key regulator of root morphogenesis across angiosperms. To better understand auxin-regulated networks underlying maize root development, we have characterized auxin-responsive transcription across two time points (30 and 120 min) and four regions of the primary root: the meristematic zone, elongation zone, cortex and stele. Hundreds of auxin-regulated genes involved in diverse biological processes were quantified in these different root regions. In general, most auxin-regulated genes are region unique and are predominantly observed in differentiated tissues compared with the root meristem. Auxin gene regulatory networks were reconstructed with these data to identify key transcription factors that may underlie auxin responses in maize roots. Additionally, Auxin-Response Factor subnetworks were generated to identify target genes that exhibit tissue or temporal specificity in response to auxin. These networks describe novel molecular connections underlying maize root development and provide a foundation for functional genomic studies in a key crop.

Diversification of heat shock transcription factors expanded thermal stress responses during early plant evolution

The copy numbers of many plant transcription factor (TF) genes substantially increased during terrestrialization. This allowed TFs to acquire new specificities and thus create gene regulatory networks (GRNs) with new biological functions to help plants adapt to terrestrial environments. Through characterizing heat shock factor (HSF) genes MpHSFA1 and MpHSFB1 in the liverwort Marchantia polymorpha, we explored how heat-responsive GRNs widened their functions in M. polymorpha and Arabidopsis thaliana. An interspecies comparison of heat-induced transcriptomes and the evolutionary rates of HSFs demonstrated the emergence and subsequent rapid evolution of HSFB prior to terrestrialization. Transcriptome and metabolome analyses of M. polymorpha HSF-null mutants revealed that MpHSFA1 controls canonical heat responses such as thermotolerance and metabolic changes. MpHSFB1 also plays essential roles in heat responses, as well as regulating developmental processes including meristem branching and antheridiophore formation. Analysis of cis-regulatory elements revealed development- and stress-related TFs that function directly or indirectly downstream of HSFB. Male gametophytes of M. polymorpha showed higher levels of thermotolerance than female gametophytes, which could be explained by different expression levels of MpHSFA1U and MpHSFA1V on sex chromosome. We propose that the diversification of HSFs is linked to the expansion of HS responses, which enabled coordinated multicellular reactions in land plants.

Physiological trait networks enhance understanding of crop growth and water use in contrasting environments

Plant function arises from a complex network of structural and physiological traits. Explicit representation of these traits, as well as their connections with other biophysical processes, is required to advance our understanding of plant-soil-climate interactions. We used the Terrestrial Regional Ecosystem Exchange Simulator (TREES) to evaluate physiological trait networks in maize. Net primary productivity (NPP) and grain yield were simulated across five contrasting climate scenarios. Simulations achieving high NPP and grain yield in high precipitation environments featured trait networks conferring high water use strategies: deep roots, high stomatal conductance at low water potential ("risky" stomatal regulation), high xylem hydraulic conductivity and high maximal leaf area index. In contrast, high NPP and grain yield was achieved in dry environments with low late-season precipitation via water conserving trait networks: deep roots, high embolism resistance and low stomatal conductance at low leaf water potential ("conservative" stomatal regulation). We suggest that our approach, which allows for the simultaneous evaluation of physiological traits, soil characteristics and their interactions (i.e., networks), has potential to improve our understanding of crop performance in different environments. In contrast, evaluating single traits in isolation of other coordinated traits does not appear to be an effective strategy for predicting plant performance.

Meeting in the Middle: Lessons and Opportunities from Studying C3-C4 Intermediates

The discovery of C₃-C₄ intermediate species nearly 50 years ago opened up a new avenue for studying the evolution of photosynthetic pathways. Intermediate species exhibit anatomical, biochemical, and physiological traits that range from C₃ to C₄. A key feature of C₃-C₄ intermediates that utilize C₂ photosynthesis is the improvement in photosynthetic efficiency compared with C₃ species. Although the recruitment of some core enzymes is shared across lineages, there is significant variability in gene expression patterns, consistent with models that suggest numerous evolutionary paths from C₃ to C₄ photosynthesis. Despite the many evolutionary trajectories, the recruitment of glycine decarboxylase for C₂ photosynthesis is likely required. As technologies enable high-throughput genotyping and phenotyping, the discovery of new C₃-C₄ intermediates species will enrich comparisons between evolutionary lineages. The investigation of C₃-C₄ intermediate species will enhance our understanding of photosynthetic mechanisms and evolutionary processes and will potentially aid in crop improvement.

Gene Co-expression Network Analysis

Gene co-expression analysis is a data analysis technique that helps identify groups of genes with similar expression patterns across several different conditions. By means of these techniques, different groups have been able to assign putative metabolic pathways and functions to understudied genes and to identify novel metabolic regulation networks for different metabolites. Some groups have even used network comparative studies to understand the evolution of these networks from green algae to land plants. In this chapter, we will go over the basic definitions required to understand network topology and gene module identification. Additionally, we offer the reader a walk-through a standard analysis pipeline as implemented in the package WGCNA that takes as input raw fastq files and obtains co-expressed gene clusters and representative gene expression patterns from each module for downstream applications.

Identification of High Nitrogen Use Efficiency Phenotype in Rice (Oryza sativa L.) Through Entire Growth Duration by Unmanned Aerial Vehicle Multispectral Imagery

Identification of high Nitrogen Use Efficiency (NUE) phenotypes has been a long-standing challenge in breeding rice and sustainable agriculture to reduce the costs of nitrogen (N) fertilizers. There are two main challenges: (1) high NUE genetic sources are biologically scarce and (2) on the technical side, few easy, non-destructive, and reliable methodologies are available to evaluate plant N variations through the entire growth duration (GD). To overcome the challenges, we captured a unique higher NUE phenotype in rice as a dynamic time-series N variation curve through the entire GD analysis by canopy reflectance data collected by Unmanned Aerial Vehicle Remote Sensing Platform (UAV-RSP) for the first time. LY9348 was a high NUE rice variety with high Nitrogen Uptake Efficiency (NUpE) and high Nitrogen Utilization Efficiency (NUtE) shown in nitrogen dosage field analysis. Its canopy nitrogen content (CNC) was analyzed by the high-throughput UAV-RSP to screen two mixed categories (51 versus 42 varieties) selected from representative higher NUE indica rice collections. Five Vegetation Indices (VIs) were compared, and the Normalized Difference Red Edge Index (NDRE) showed the highest correlation with CNC (r = 0.80). Six key developmental stages of rice varieties were compared from transplantation to maturation, and the high NUE phenotype of LY9348 was shown as a dynamic N accumulation curve, where it was moderately high during the vegetative developmental stages but considerably higher in the reproductive developmental stages with a slower reduction rate. CNC curves of different rice varieties were analyzed to construct two non-linear regression models between N% or N% × leaf area index (LAI) with NDRE separately. Both models could determine the specific phenotype with the coefficient of determination (R 2) above 0.61 (Model I) and 0.86 (Model II). Parameters influencing the correlation accuracy between NDRE and N% were found to be better by removing the tillering stage data, separating the short and long GD varieties for the analysis and adding canopy structures, such as LAI, into consideration. The high NUE phenotype of LY9348 could be traced and reidentified across different years, locations, and genetic germplasm groups. Therefore, an effective and reliable high-throughput method was proposed for assisting the selection of the high NUE breeding phenotype.

Identification of Key Genes in 'Luang Pratahn', Thai Salt-Tolerant Rice, Based on Time-Course Data and Weighted Co-expression Networks

Salinity is an important environmental factor causing a negative effect on rice production. To prevent salinity effects on rice yields, genetic diversity concerning salt tolerance must be evaluated. In this study, we investigated the salinity responses of rice (Oryza sativa) to determine the critical genes. The transcriptomes of 'Luang Pratahn' rice, a local Thai rice variety with high salt tolerance, were used as a model for analyzing and identifying the key genes responsible for salt-stress tolerance. Based on 3' Tag-Seq data from the time course of salt-stress treatment, weighted gene co-expression network analysis was used to identify key genes in gene modules. We obtained 1,386 significantly differentially expressed genes in eight modules. Among them, six modules indicated a significant correlation within 6, 12, or 48h after salt stress. Functional and pathway enrichment analysis was performed on the co-expressed genes of interesting modules to reveal which genes were mainly enriched within important functions for salt-stress responses. To identify the key genes in salt-stress responses, we considered the two-state co-expression networks, normal growth conditions, and salt stress to investigate which genes were less important in a normal situation but gained more impact under stress. We identified key genes for the response to biotic and abiotic stimuli and tolerance to salt stress. Thus, these novel genes may play important roles in salinity tolerance and serve as potential biomarkers to improve salt tolerance cultivars.

Large potential for crop production adaptation depends on available future varieties

Climate change affects global agricultural production and threatens food security. Faster phenological development of crops due to climate warming is one of the main drivers for potential future yield reductions. To counter the effect of faster maturity, adapted varieties would require more heat units to regain the previous growing period length. In this study, we investigate the effects of variety adaptation on global caloric production under four different future climate change scenarios for maize, rice, soybean, and wheat. Thereby, we empirically identify areas that could require new varieties and areas where variety adaptation could be achieved by shifting existing varieties into new regions. The study uses an ensemble of seven global gridded crop models and five CMIP6 climate models. We found that 39% (SSP5-8.5) of global cropland could require new crop varieties to avoid yield loss from climate change by the end of the century. At low levels of warming (SSP1-2.6), 85% of currently cultivated land can draw from existing varieties to shift within an agro-ecological zone for adaptation. The assumptions on available varieties for adaptation have major impacts on the effectiveness of variety adaptation, which could more than half in SSP5-8.5. The results highlight that region-specific breeding efforts are required to allow for a successful adaptation to climate change.

Evolutionarily conserved hierarchical gene regulatory networks for plant salt stress response

Plant cells constantly alter their gene expression profiles to respond to environmental fluctuations. These continuous adjustments are regulated by multi-hierarchical networks of transcription factors. To understand how such gene regulatory networks (GRNs) have stabilized evolutionarily while allowing for species-specific responses, we compare the GRNs underlying salt response in the early-diverging and late-diverging plants Marchantia polymorpha and Arabidopsis thaliana. Salt-responsive GRNs, constructed on the basis of the temporal transcriptional patterns in the two species, share common trans-regulators but exhibit an evolutionary divergence in cis-regulatory sequences and in the overall network sizes. In both species, WRKY-family transcription factors and their feedback loops serve as central nodes in salt-responsive GRNs. The divergent cis-regulatory sequences of WRKY-target genes are probably associated with the expansion in network size, linking salt stress to tissue-specific developmental and physiological responses. The WRKY modules and highly linked WRKY feedback loops have been preserved widely in other plants, including rice, while keeping their binding-motif sequences mutable. Together, the conserved trans-regulators and the quickly evolving cis-regulatory sequences allow salt-responsive GRNs to adapt over a long evolutionary timescale while maintaining some consistent regulatory structure. This strategy may benefit plants as they adapt to changing environments.

Dataset on the Effects of Different Pre-Harvest Factors on the Metabolomics Profile of Lettuce (Lactuca sativa L.) Leaves

The study of the relationship between cultivated plants and environmental factors can provide information ranging from a deeper understanding of the plant biological system to the development of more effective management strategies for improving yield, quality, and sustainability of the produce. In this article, we present a comprehensive metabolomics dataset of two phytochemically divergent lettuce (Lactuca sativa L.) butterhead varieties under different growing conditions. Plants were cultivated in hydroponics in a growth chamber with ambient control. The pre-harvest factors that were independently investigated were light intensity (two levels), the ionic strength of the nutrient solutions (three levels), and the molar ratio of three macroelements (K, Mg, and Ca) in the nutrient solution (three levels). We used an untargeted, mass-spectrometry-based approach to characterize the metabolomics profiles of leaves harvested 19 days after transplant. The data revealed the ample impact on both primary and secondary metabolism and its range of variation. Moreover, our dataset is useful for uncovering the complex effects of the genotype, the environmental factor(s), and their interaction, which may deserve further investigation.

Network-based approaches for understanding gene regulation and function in plants

Expression reprogramming directed by transcription factors is a primary gene regulation underlying most aspects of the biology of any organism. Our views of how gene regulation is coordinated are dramatically changing thanks to the advent and constant improvement of high-throughput profiling and transcriptional network inference methods: from activities of individual genes to functional interactions across genes. These technical and analytical advances can reveal the topology of transcriptional networks in which hundreds of genes are hierarchically regulated by multiple transcription factors at systems level. Here we review the state of the art of experimental and computational methods used in plant biology research to obtain large-scale datasets and model transcriptional networks. Examples of direct use of these network models and perspectives on their limitations and future directions are also discussed.

Photosynthesis: basics, history and modelling

BACKGROUND

With limited agricultural land and increasing human population, it is essential to enhance overall photosynthesis and thus productivity. Oxygenic photosynthesis begins with light absorption, followed by excitation energy transfer to the reaction centres, primary photochemistry, electron and proton transport, NADPH and ATP synthesis, and then CO2 fixation (Calvin-Benson cycle, as well as Hatch-Slack cycle). Here we cover some of the discoveries related to this process, such as the existence of two light reactions and two photosystems connected by an electron transport 'chain' (the Z-scheme), chemiosmotic hypothesis for ATP synthesis, water oxidation clock for oxygen evolution, steps for carbon fixation, and finally the diverse mechanisms of regulatory processes, such as 'state transitions' and 'non-photochemical quenching' of the excited state of chlorophyll a.

SCOPE

In this review, we emphasize that mathematical modelling is a highly valuable tool in understanding and making predictions regarding photosynthesis. Different mathematical models have been used to examine current theories on diverse photosynthetic processes; these have been validated through simulation(s) of available experimental data, such as chlorophyll a fluorescence induction, measured with fluorometers using continuous (or modulated) exciting light, and absorbance changes at 820 nm (ΔA820) related to redox changes in P700, the reaction centre of photosystem I.

CONCLUSIONS

We highlight here the important role of modelling in deciphering and untangling complex photosynthesis processes taking place simultaneously, as well as in predicting possible ways to obtain higher biomass and productivity in plants, algae and cyanobacteria.

Tripartite mutualisms as models for understanding plant-microbial interactions

All plants host diverse microbial assemblages that shape plant health, productivity, and function. While some microbial effects are attributable to particular symbionts, interactions among plant-associated microbes can nonadditively affect plant fitness and traits in ways that cannot be predicted from pairwise interactions. Recent research into tripartite plant-microbe mutualisms has provided crucial insight into this nonadditivity and the mechanisms underlying plant interactions with multiple microbes. Here, we discuss how interactions among microbial mutualists affect plant performance, highlight consequences of biotic and abiotic context-dependency for nonadditive outcomes, and summarize burgeoning efforts to determine the molecular bases of how plants regulate establishment, resource exchange, and maintenance of tripartite interactions. We conclude with four goals for future tripartite studies that will advance our overall understanding of complex plant-microbial interactions.

Multiscale computational models can guide experimentation and targeted measurements for crop improvement

Computational models of plants have identified gaps in our understanding of biological systems, and have revealed ways to optimize cellular processes or organ-level architecture to increase productivity. Thus, computational models are learning tools that help direct experimentation and measurements. Models are simplifications of complex systems, and often simulate specific processes at single scales (e.g. temporal, spatial, organizational, etc.). Consequently, single-scale models are unable to capture the critical cross-scale interactions that result in emergent properties of the system. In this perspective article, we contend that to accurately predict how a plant will respond in an untested environment, it is necessary to integrate mathematical models across biological scales. Computationally mimicking the flow of biological information from the genome to the phenome is an important step in discovering new experimental strategies to improve crops. A key challenge is to connect models across biological, temporal and computational (e.g. CPU versus GPU) scales, and then to visualize and interpret integrated model outputs. We address this challenge by describing the efforts of the international Crops in silico consortium.

Aromatic Amino Acids: A Complex Network Ripe for Future Exploration

In plants, high carbon flux is committed to the biosynthesis of phenylalanine, tyrosine, and tryptophan, owing to their roles not only in the production of proteins, but also as precursors to thousands of primary and specialized metabolites. The core plastidial pathways that supply the majority of aromatic amino acids (AAAs) have previously been described in detail. More recently, the discovery of cytosolic enzymes contributing to overall AAA biosynthesis, as well as the identification of intracellular transporters and the continuing elucidation of transcriptional and post-transcriptional regulatory mechanisms, have revealed the complexity of this intercompartmental metabolic network. Here, we review the latest breakthroughs in AAA production and use the newest findings to highlight both longstanding and newly developed questions.

Gene Regulatory Network Guided Investigations and Engineering of Storage Root Development in Root Crops

The plasticity of plant development relies on its ability to balance growth and stress resistance. To do this, plants have established highly coordinated gene regulatory networks (GRNs) of the transcription factors and signaling components involved in developmental processes and stress responses. In root crops, yields of storage roots are mainly determined by secondary growth driven by the vascular cambium. In relation to this, a dynamic yet intricate GRN should operate in the vascular cambium, in coordination with environmental changes. Despite the significance of root crops as food sources, GRNs wired to mediate secondary growth in the storage root have just begun to emerge, specifically with the study of the radish. Gene expression data available with regard to other important root crops are not detailed enough for us directly to infer underlying molecular mechanisms. Thus, in this review, we provide a general overview of the regulatory programs governing the development and functions of the vascular cambium in model systems, and the role of the vascular cambium on the growth and yield potential of the storage roots in root crops. We then undertake a reanalysis of recent gene expression data generated for major root crops and discuss common GRNs involved in the vascular cambium-driven secondary growth in storage roots using the wealth of information available in Arabidopsis. Finally, we propose future engineering schemes for improving root crop yields by modifying potential key nodes in GRNs.