The Spring of Systems Biology-Driven Breeding

Innovative computational tools provide new insights into the polyploid wheat genome

Bread wheat (Triticum aestivum) is an important crop and serves as a significant source of protein and calories for humans, worldwide. Nevertheless, its large and allopolyploid genome poses constraints on genetic improvement. The complex reticulate evolutionary history and the intricacy of genomic resources make the deciphering of the functional genome considerably more challenging. Recently, we have developed a comprehensive list of versatile computational tools with the integration of statistical models for dissecting the polyploid wheat genome. Here, we summarize the methodological innovations and applications of these tools and databases. A series of step-by-step examples illustrates how these tools can be utilized for dissecting wheat germplasm resources and unveiling functional genes associated with important agronomic traits. Furthermore, we outline future perspectives on new advanced tools and databases, taking into consideration the unique features of bread wheat, to accelerate genomic-assisted wheat breeding.

Data-driven approaches to improve water-use efficiency and drought resistance in crop plants

With the increasing population, there lies a pressing demand for food, feed and fibre, while the changing climatic conditions pose severe challenges for agricultural production worldwide. Water is the lifeline for crop production; thus, enhancing crop water-use efficiency (WUE) and improving drought resistance in crop varieties are crucial for overcoming these challenges. Genetically-driven improvements in yield, WUE and drought tolerance traits can buffer the worst effects of climate change on crop production in dry areas. While traditional crop breeding approaches have delivered impressive results in increasing yield, the methods remain time-consuming and are often limited by the existing allelic variation present in the germplasm. Significant advances in breeding and high-throughput omics technologies in parallel with smart agriculture practices have created avenues to dramatically speed up the process of trait improvement by leveraging the vast volumes of genomic and phenotypic data. For example, individual genome and pan-genome assemblies, along with transcriptomic, metabolomic and proteomic data from germplasm collections, characterised at phenotypic levels, could be utilised to identify marker-trait associations and superior haplotypes for crop genetic improvement. In addition, these omics approaches enable the identification of genes involved in pathways leading to the expression of a trait, thereby providing an understanding of the genetic, physiological and biochemical basis of trait variation. These data-driven gene discoveries and validation approaches are essential for crop improvement pipelines, including genomic breeding, speed breeding and gene editing. Herein, we provide an overview of prospects presented using big data-driven approaches (including artificial intelligence and machine learning) to harness new genetic gains for breeding programs and develop drought-tolerant crop varieties with favourable WUE and high-yield potential traits.

Crop germplasm: Current challenges, physiological-molecular perspective, and advance strategies towards development of climate-resilient crops

Germplasm is a long-term resource management mission and investment for civilization. An estimated ∼7.4 million accessions are held in 1750 plant germplasm centres around the world; yet, only 2% of these assets have been utilized as plant genetic resources (PGRs). According to recent studies, the current food yield trajectory will be insufficient to feed the world's population in 2050. Additionally, possible negative effects in terms of crop failure because of climate change are already being experienced across the world. Therefore, it is necessary to reconciliation of research advancement and innovation of practices for further exploration of the potential of crop germplasm especially for the complex traits associated with yield such as water- and nitrogen use efficiency. In this review, we tried to address current challenges, research gaps, physiological and molecular aspects of two broad spectrum complex traits such as water- and nitrogen-use efficiency, and advanced integrated strategies that could provide a platform for combined stress management for climate-smart crop development. Additionally, recent development in technologies that are directly related to germplasm characterization was highlighted for further molecular utilization towards the development of elite varieties.

The Global Assessment of Oilseed Brassica Crop Species Yield, Yield Stability and the Underlying Genetics

The global demand for oilseeds is increasing along with the human population. The family of Brassicaceae crops are no exception, typically harvested as a valuable source of oil, rich in beneficial molecules important for human health. The global capacity for improving Brassica yield has steadily risen over the last 50 years, with the major crop Brassica napus (rapeseed, canola) production increasing to ~72 Gt in 2020. In contrast, the production of Brassica mustard crops has fluctuated, rarely improving in farming efficiency. The drastic increase in global yield of B. napus is largely due to the demand for a stable source of cooking oil. Furthermore, with the adoption of highly efficient farming techniques, yield enhancement programs, breeding programs, the integration of high-throughput phenotyping technology and establishing the underlying genetics, B. napus yields have increased by >450 fold since 1978. Yield stability has been improved with new management strategies targeting diseases and pests, as well as by understanding the complex interaction of environment, phenotype and genotype. This review assesses the global yield and yield stability of agriculturally important oilseed Brassica species and discusses how contemporary farming and genetic techniques have driven improvements.

Future Challenges in Plant Systems Biology

Plant systems biology is currently facing several important challenges, whose nature depend on the considered frame of reference and associated scale. This review covers some of the issues associated respectively with the molecular, tissue, and whole-plant scales, as well as discusses the potential for latest advances in synthetic biology and machine-learning methods to be of use in the future of plant systems biology.

Exploitation of Drought Tolerance-Related Genes for Crop Improvement

Drought has become a major threat to food security, because it affects crop growth and development. Drought tolerance is an important quantitative trait, which is regulated by hundreds of genes in crop plants. In recent decades, scientists have made considerable progress to uncover the genetic and molecular mechanisms of drought tolerance, especially in model plants. This review summarizes the evaluation criteria for drought tolerance, methods for gene mining, characterization of genes related to drought tolerance, and explores the approaches to enhance crop drought tolerance. Collectively, this review illustrates the application prospect of these genes in improving the drought tolerance breeding of crop plants.

De-novo Domestication for Improving Salt Tolerance in Crops

Global agriculture production is under serious threat from rapidly increasing population and adverse climate changes. Food security is currently a huge challenge to feed 10 billion people by 2050. Crop domestication through conventional approaches is not good enough to meet the food demands and unable to fast-track the crop yields. Also, intensive breeding and rigorous selection of superior traits causes genetic erosion and eliminates stress-responsive genes, which makes crops more prone to abiotic stresses. Salt stress is one of the most prevailing abiotic stresses that poses severe damages to crop yield around the globe. Recent innovations in state-of-the-art genomics and transcriptomics technologies have paved the way to develop salinity tolerant crops. De novo domestication is one of the promising strategies to produce superior new crop genotypes through exploiting the genetic diversity of crop wild relatives (CWRs). Next-generation sequencing (NGS) technologies open new avenues to identifying the unique salt-tolerant genes from the CWRs. It has also led to the assembly of highly annotated crop pan-genomes to snapshot the full landscape of genetic diversity and recapture the huge gene repertoire of a species. The identification of novel genes alongside the emergence of cutting-edge genome editing tools for targeted manipulation renders de novo domestication a way forward for developing salt-tolerance crops. However, some risk associated with gene-edited crops causes hurdles for its adoption worldwide. Halophytes-led breeding for salinity tolerance provides an alternative strategy to identify extremely salt tolerant varieties that can be used to develop new crops to mitigate salinity stress.

Next-Generation Breeding Strategies for Climate-Ready Crops

Climate change is a threat to global food security due to the reduction of crop productivity around the globe. Food security is a matter of concern for stakeholders and policymakers as the global population is predicted to bypass 10 billion in the coming years. Crop improvement via modern breeding techniques along with efficient agronomic practices innovations in microbiome applications, and exploiting the natural variations in underutilized crops is an excellent way forward to fulfill future food requirements. In this review, we describe the next-generation breeding tools that can be used to increase crop production by developing climate-resilient superior genotypes to cope with the future challenges of global food security. Recent innovations in genomic-assisted breeding (GAB) strategies allow the construction of highly annotated crop pan-genomes to give a snapshot of the full landscape of genetic diversity (GD) and recapture the lost gene repertoire of a species. Pan-genomes provide new platforms to exploit these unique genes or genetic variation for optimizing breeding programs. The advent of next-generation clustered regularly interspaced short palindromic repeat/CRISPR-associated (CRISPR/Cas) systems, such as prime editing, base editing, and de nova domestication, has institutionalized the idea that genome editing is revamped for crop improvement. Also, the availability of versatile Cas orthologs, including Cas9, Cas12, Cas13, and Cas14, improved the editing efficiency. Now, the CRISPR/Cas systems have numerous applications in crop research and successfully edit the major crop to develop resistance against abiotic and biotic stress. By adopting high-throughput phenotyping approaches and big data analytics tools like artificial intelligence (AI) and machine learning (ML), agriculture is heading toward automation or digitalization. The integration of speed breeding with genomic and phenomic tools can allow rapid gene identifications and ultimately accelerate crop improvement programs. In addition, the integration of next-generation multidisciplinary breeding platforms can open exciting avenues to develop climate-ready crops toward global food security.

Systems biology for crop improvement

In recent years, generation of large-scale data from genome, transcriptome, proteome, metabolome, epigenome, and others, has become routine in several plant species. Most of these datasets in different crop species, however, were studied independently and as a result, full insight could not be gained on the molecular basis of complex traits and biological networks. A systems biology approach involving integration of multiple omics data, modeling, and prediction of the cellular functions is required to understand the flow of biological information that underlies complex traits. In this context, systems biology with multiomics data integration is crucial and allows a holistic understanding of the dynamic system with the different levels of biological organization interacting with external environment for a phenotypic expression. Here, we present recent progress made in the area of various omics studies-integrative and systems biology approaches with a special focus on application to crop improvement. We have also discussed the challenges and opportunities in multiomics data integration, modeling, and understanding of the biology of complex traits underpinning yield and stress tolerance in major cereals and legumes.

Genetic Improvement in Sunflower Breeding—Integrated Omics Approach

Foresight in climate change and the challenges ahead requires a systematic approach to sunflower breeding that will encompass all available technologies. There is a great scarcity of desirable genetic variation, which is in fact undiscovered because it has not been sufficiently researched as detection and designing favorable genetic variation largely depends on thorough genome sequencing through broad and deep resequencing. Basic exploration of genomes is insufficient to find insight about important physiological and molecular mechanisms unique to crops. That is why integrating information from genomics, epigenomics, transcriptomics, proteomics, metabolomics and phenomics enables a comprehensive understanding of the molecular mechanisms in the background of architecture of many important quantitative traits. Omics technologies offer novel possibilities for deciphering the complex pathways and molecular profiling through the level of systems biology and can provide important answers that can be utilized for more efficient breeding of sunflower. In this review, we present omics profiling approaches in order to address their possibilities and usefulness as a potential breeding tools in sunflower genetic improvement.

Genomics and breeding innovations for enhancing genetic gain for climate resilience and nutrition traits

KEY MESSAGE

Integrating genomics technologies and breeding methods to tweak core parameters of the breeder's equation could accelerate delivery of climate-resilient and nutrient rich crops for future food security. Accelerating genetic gain in crop improvement programs with respect to climate resilience and nutrition traits, and the realization of the improved gain in farmers' fields require integration of several approaches. This article focuses on innovative approaches to address core components of the breeder's equation. A prerequisite to enhancing genetic variance (σ2g) is the identification or creation of favorable alleles/haplotypes and their deployment for improving key traits. Novel alleles for new and existing target traits need to be accessed and added to the breeding population while maintaining genetic diversity. Selection intensity (i) in the breeding program can be improved by testing a larger population size, enabled by the statistical designs with minimal replications and high-throughput phenotyping. Selection priorities and criteria to select appropriate portion of the population too assume an important role. The most important component of breeder's equation is heritability (h2). Heritability estimates depend on several factors including the size and the type of population and the statistical methods. The present article starts with a brief discussion on the potential ways to enhance σ2g in the population. We highlight statistical methods and experimental designs that could improve trait heritability estimation. We also offer a perspective on reducing the breeding cycle time (t), which could be achieved through the selection of appropriate parents, optimizing the breeding scheme, rapid fixation of target alleles, and combining speed breeding with breeding programs to optimize trials for release. Finally, we summarize knowledge from multiple disciplines for enhancing genetic gains for climate resilience and nutritional traits.

RNA Sequencing Analyses for Deciphering Potato Molecular Responses

Understanding the molecular mechanisms of potato development and responses to environmental stressors is of utmost importance for achieving stable crop yields. RNA sequencing (RNA-Seq) provides an insight into responses of all of the organism genes to the environmental and developmental cues and thus provides insights into underlying modes of action. In this chapter, we guide a researcher through some of the most important steps in the analysis of transcriptomics data. The initial topic of experimental design is followed by a more wet-lab-oriented section on RNA-Seq sample preparation. Next, we present intermediate steps of data retrieval, quality control, mapping, and differential expression of the dataset and a section on how to expose your data to the public (i.e., public repositories) and make it findable, accessible, interoperable, and reusable (FAIR). In the last four sections, we describe specific tools or Web applications, which ease the exploration of generated results in the context of their gene function and network-based visualizations, specifically GoMapMan, GSEA, DiNAR, and Biomine Explorer. All sections are accompanied by potato dataset examples and include general hints and tricks, as well as potato specificities that one should be aware of.

Genomic interventions for sustainable agriculture

Agricultural production faces a Herculean challenge to feed the increasing global population. Food production systems need to deliver more with finite land and water resources while exerting the least negative influence on the ecosystem. The unpredictability of climate change and consequent changes in pests/pathogens dynamics aggravate the enormity of the challenge. Crop improvement has made significant contributions towards food security, and breeding climate-smart cultivars are considered the most sustainable way to accelerate food production. However, a fundamental change is needed in the conventional breeding framework in order to respond adequately to the growing food demands. Progress in genomics has provided new concepts and tools that hold promise to make plant breeding procedures more precise and efficient. For instance, reference genome assemblies in combination with germplasm sequencing delineate breeding targets that could contribute to securing future food supply. In this review, we highlight key breakthroughs in plant genome sequencing and explain how the presence of these genome resources in combination with gene editing techniques has revolutionized the procedures of trait discovery and manipulation. Adoption of new approaches such as speed breeding, genomic selection and haplotype-based breeding could overcome several limitations of conventional breeding. We advocate that strengthening varietal release and seed distribution systems will play a more determining role in delivering genetic gains at farmer's field. A holistic approach outlined here would be crucial to deliver steady stream of climate-smart crop cultivars for sustainable agriculture.

SEGN: Inferring real-time gene networks mediating phenotypic plasticity

The capacity of an organism to alter its phenotype in response to environmental perturbations changes over developmental time and is a process determined by multiple genes that are co-expressed in intricate but organized networks. Characterizing the spatiotemporal change of such gene networks can offer insight into the genomic signatures underlying organismic adaptation, but it represents a major methodological challenge. Here, we integrate the holistic view of systems biology and the interactive notion of evolutionary game theory to reconstruct so-called systems evolutionary game networks (SEGN) that can autonomously detect, track, and visualize environment-induced gene networks along the time axis. The SEGN overcomes the limitations of traditional approaches by inferring context-specific networks, encapsulating bidirectional, signed, and weighted gene-gene interactions into fully informative networks, and monitoring the process of how networks topologically alter across environmental and developmental cues. Based on the design principle of SEGN, we perform a transcriptional plasticity study by culturing Euphrates poplar, a tree that can grow in the saline desert, in saline-free and saline-stress conditions. SEGN characterize previously unknown gene co-regulation that modulates the time trajectories of the trees' response to salt stress. As a marriage of multiple disciplines, SEGN shows its potential to interpret gene interdependence, predict how transcriptional co-regulation responds to various regimes, and provides a hint for exploring the mass, energetic, or signal basis that drives various types of gene interactions.

TB1: from domestication gene to tool for many trades

This article comments on: Dixon LE, Pasquariello M, Boden SA. 2020. TEOSINTE BRANCHED1 regulates height and stem internode length in bread wheat. Journal of Experimental Botany 71, 4742–4750.

5Gs for crop genetic improvement

Here we propose a 5G breeding approach for bringing much-needed disruptive changes to crop improvement. These 5Gs are Genome assembly, Germplasm characterization, Gene function identification, Genomic breeding (GB), and Gene editing (GE). In our view, it is important to have genome assemblies available for each crop and a deep collection of germplasm characterized at sequencing and agronomic levels for identification of marker-trait associations and superior haplotypes. Systems biology and sequencing-based mapping approaches can be used to identify genes involved in pathways leading to the expression of a trait, thereby providing diagnostic markers for target traits. These genes, markers, haplotypes, and genome-wide sequencing data may be utilized in GB and GE methodologies in combination with a rapid cycle breeding strategy.

Engineering Synthetic Signaling in Plants

Synthetic signaling is a branch of synthetic biology that aims to understand native genetic regulatory mechanisms and to use these insights to engineer interventions and devices that achieve specified design parameters. Applying synthetic signaling approaches to plants offers the promise of mitigating the worst effects of climate change and providing a means to engineer crops for entirely novel environments, such as those in space travel. The ability to engineer new traits using synthetic signaling methods will require standardized libraries of biological parts and methods to assemble them; the decoupling of complex processes into simpler subsystems; and mathematical models that can accelerate the design-build-test-learn cycle. The field of plant synthetic signaling is relatively new, but it is poised for rapid advancement. Translation from the laboratory to the field is likely to be slowed, however, by the lack of constructive dialogue between researchers and other stakeholders.

Empowering crop resilience to environmental multiple stress through the modulation of key response components

Crop plants have developed a multitude of defense and adaptation responses to protect themselves against invading pathogens and challenging environmental stresses, mostly operating jointly. The plant perception of overall stress induces a coordinated response mediated by complex signaling networks. Experimental evidences proved that plant response to combined biotic and abiotic stresses substantially diverge from the responses to individual stresses. Moreover, the cross-talk of signaling pathways involved in responding to biotic and abiotic stresses is pivoted on several converging elements able to simultaneously modulate the timing and amplitude of the overall plant response. Comprehensively, the interaction between biotic and abiotic stresses can dramatically changes the plant response to the individual stress and the phenotypical outcome of each stress factor. System biology and data mining can synergistically help biologists in finding out regulative mechanisms and key genes controlling the response to biotic and abiotic stresses. Deploying new genetic engineering solutions can rely on the modification of genes involved in resistance/tolerance processes and/or in the modulation of regulatory elements. Finally, a model of the engineered crop for enhanced tolerance to pressures resulting from invasive pathogens and abiotic constraints in semiarid and warm environment is discussed.

Haplotype analysis of key genes governing grain yield and quality traits across 3K RG panel reveals scope for the development of tailor-made rice with enhanced genetic gains

Though several genes governing various major traits have been reported in rice, their superior haplotype combinations for developing ideal variety remains elusive. In this study, haplotype analysis of 120 previously functionally characterized genes, influencing grain yield (87 genes) and grain quality (33 genes) revealed significant variations in the 3K rice genome (RG) panel. For selected genes, meta-expression analysis using already available datasets along with co-expression network provided insights at systems level. Also, we conducted candidate gene based association study for the 120 genes and identified 21 strongly associated genes governing 10-grain yield and quality traits. We report superior haplotypes upon phenotyping the subset of 3K RG panel, SD1-H8 with haplotype frequency (HF) of 30.13% in 3K RG panel, MOC1-H9 (HF: 23.08%), IPA1-H14 (HF: 6.64%), DEP3-H2 (HF: 5.59%), DEP1-H2 (HF: 37.53%), SP1-H3 (HF: 5.05%), LAX1-H5 (HF: 1.56%), LP-H13 (3.64%), OSH1-H4 (5.52%), PHD1-H14 (HF: 15.21%), AGO7-H15 (HF: 3.33%), ROC5-H2 (31.42%), RSR1-H8 (HF: 4.20%) and OsNAS3-H2 (HF: 1.00%). For heading date, Ghd7-H8 (HF: 3.08%), TOB1-H10 (HF: 4.60%) flowered early, Ghd7-H14 (HF: 42.60%), TRX1-H9 (HF: 27.97%), OsVIL3-H14 (HF: 1.72%) for medium duration flowering, while Ghd7-H6 (HF: 1.65%), SNB-H9 (HF: 9.35%) were late flowering. GS5-H4 (HF: 65.84%) attributed slender, GS5-H5 (HF: 29.00%), GW2-H2 (HF: 4.13%) were medium slender and GS5-H9 (HF: 2.15%) for bold grains. Furthermore, haplotype analysis explained possible genetic basis for superiority of selected mega-varieties. Overall, this study suggests the possibility for developing next-generation tailor-made rice with superior haplotype combinations of target genes suiting future food and nutritional demands via haplotype-based breeding.

Infrastructures of systems biology that facilitate functional genomic study in rice

Rice (Oryza sativa L.) is both a major staple food for the worldwide population and a model crop plant for studying the mode of action of agronomically valuable traits, providing information that can be applied to other crop plants. Due to the development of high-throughput technologies such as next generation sequencing and mass spectrometry, a huge mass of multi-omics data in rice has been accumulated. Through the integration of those data, systems biology in rice is becoming more advanced.To facilitate such systemic approaches, we have summarized current resources, such as databases and tools, for systems biology in rice. In this review, we categorize the resources using six omics levels: genomics, transcriptomics, proteomics, metabolomics, integrated omics, and functional genomics. We provide the names, websites, references, working states, and number of citations for each individual database or tool and discuss future prospects for the integrated understanding of rice gene functions.

Next-Generation Sequencing Accelerates Crop Gene Discovery

The identification and isolation of genes underlying quantitative trait loci (QTLs) associated with agronomic traits in crops have been recently accelerated thanks to next-generation sequencing (NGS)-based technologies combined with plant genetics. With NGS, various revisited genetic approaches, which benefited from higher marker density, have been elaborated. These approaches improved resolution in QTL position and assisted in determining functional causative variations in genes. Examples of QTLs/genes associated with agronomic traits in crops and identified using different strategies based on whole-genome sequencing (WGS)/whole-genome resequencing (WGR) or RNA-seq are presented and discussed in this review. More specifically, we summarize and illustrate how NGS boosted bulk-segregant analysis (BSA), expression profiling, and the construction of polymorphism databases to facilitate the detection of QTLs and causative genes.

A Computational Model for Inferring QTL Control Networks Underlying Developmental Covariation

How one trait developmentally varies as a function of others shapes a spectrum of biological phenomena. Despite its importance to trait dissection, the understanding of whether and how genes mediate such developmental covariation is poorly understood. We integrate developmental allometry equations into the functional mapping framework to map specific QTLs that govern the correlated development of different traits. Based on evolutionary game theory, we assemble and contextualize these QTLs into an intricate but organized network coded by bidirectional, signed, and weighted QTL-QTL interactions. We use this approach to map shoot height-diameter allometry QTLs in an ornamental woody species, mei (Prunus mume). We detect "pioneering" QTLs (piQTLs) and "maintaining" QTLs (miQTLs) that determine how shoot height varies with diameter and how shoot diameter varies with height, respectively. The QTL networks inferred can visualize how each piQTL regulates others to promote height growth at a cost of diameter growth, how miQTL regulates others to benefit radial growth at a cost of height growth, and how piQTLs and miQTLs regulate each other to form a pleiotropic web of primary and secondary growth in trees. Our approach provides a unique gateway to explore the genetic architecture of developmental covariation, a widespread phenomenon in nature.