Genomics-based strategies for the use of natural variation in the improvement of crop metabolism

Plant Metabolomics: Current Initiatives and Future Prospects

Plant metabolomics is a rapidly advancing field of plant sciences and systems biology. It involves comprehensive analyses of small molecules (metabolites) in plant tissues and cells. These metabolites include a wide range of compounds, such as sugars, amino acids, organic acids, secondary metabolites (e.g., alkaloids and flavonoids), lipids, and more. Metabolomics allows an understanding of the functional roles of specific metabolites in plants' physiology, development, and responses to biotic and abiotic stresses. It can lead to the identification of metabolites linked with specific traits or functions. Plant metabolic networks and pathways can be better understood with the help of metabolomics. Researchers can determine how plants react to environmental cues or genetic modifications by examining how metabolite profiles change under various crop stages. Metabolomics plays a major role in crop improvement and biotechnology. Integrating metabolomics data with other omics data (genomics, transcriptomics, and proteomics) provides a more comprehensive perspective of plant biology. This systems biology approach enables researchers to understand the complex interactions within organisms.

Approach strategies and application of metabolomics to biotechnology in plants

Metabolomics refers to the technology for the comprehensive analysis of metabolites and low-molecular-weight compounds in a biological system, such as cells or tissues. Metabolites play an important role in biological phenomena through their direct involvement in the regulation of physiological mechanisms, such as maintaining cell homeostasis or signal transmission through protein-protein interactions. The current review aims provide a framework for how the integrated analysis of metabolites, their functional actions and inherent biological information can be used to understand biological phenomena related to the regulation of metabolites and how this information can be applied to safety assessments of crops created using biotechnology. Advancement in technology and analytical instrumentation have led new ways to examine the convergence between biology and chemistry, which has yielded a deeper understanding of complex biological phenomena. Metabolomics can be utilized and applied to safety assessments of biotechnology products through a systematic approach using metabolite-level data processing algorithms, statistical techniques, and database development. The integration of metabolomics data with sequencing data is a key step towards improving additional phenotypical evidence to elucidate the degree of environmental affects for variants found in genome associated with metabolic processes. Moreover, information analysis technology such as big data, machine learning, and IT investment must be introduced to establish a system for data extraction, selection, and metabolomic data analysis for the interpretation of biological implications of biotechnology innovations. This review outlines the integrity of metabolomics assessments in determining the consequences of genetic engineering and biotechnology in plants.

Can Metabolomic Approaches Become a Tool for Improving Early Plant Disease Detection and Diagnosis with Modern Remote Sensing Methods? A Review

The various areas of ultra-sensitive remote sensing research equipment development have provided new ways for assessing crop states. However, even the most promising areas of research, such as hyperspectral remote sensing or Raman spectrometry, have not yet led to stable results. In this review, the main methods for early plant disease detection are discussed. The best proven existing techniques for data acquisition are described. It is discussed how they can be applied to new areas of knowledge. The role of metabolomic approaches in the application of modern methods for early plant disease detection and diagnosis is reviewed. A further direction for experimental methodological development is indicated. The ways to increase the efficiency of modern early plant disease detection remote sensing methods through metabolomic data usage are shown. This article provides an overview of modern sensors and technologies for assessing the biochemical state of crops as well as the ways to apply them in synergy with existing data acquisition and analysis technologies for early plant disease detection.

Understanding source-sink interactions: Progress in model plants and translational research to crops

Agriculture is facing a massive increase in demand per hectare as a result of an ever-expanding population and environmental deterioration. While we have learned much about how environmental conditions and diseases impact crop yield, until recently considerably less was known concerning endogenous factors, including within-plant nutrient allocation. In this review, we discuss studies of source-sink interactions covering both fundamental research in model systems under controlled growth conditions and how the findings are being translated to crop plants in the field. In this respect we detail efforts aimed at improving and/or combining C₃, C₄, and CAM modes of photosynthesis, altering the chloroplastic electron transport chain, modulating photorespiration, adopting bacterial/algal carbon-concentrating mechanisms, and enhancing nitrogen- and water-use efficiencies. Moreover, we discuss how modulating TCA cycle activities and primary metabolism can result in increased rates of photosynthesis and outline the opportunities that evaluating natural variation in photosynthesis may afford. Although source, transport, and sink functions are all covered in this review, we focus on discussing source functions because the majority of research has been conducted in this field. Nevertheless, considerable recent evidence, alongside the evidence from classical studies, demonstrates that both transport and sink functions are also incredibly important determinants of yield. We thus describe recent evidence supporting this notion and suggest that future strategies for yield improvement should focus on combining improvements in each of these steps to approach yield optimization.

Disease resistance conferred by components of essential chrysanthemum oil and the epigenetic regulation of OsTPS1

The sesquiterpene alpha-bisabolol is the predominant active ingredient in essential oils that are highly valued in the cosmetics industry due to its wound healing, anti-inflammatory, and skin-soothing properties. Alpha-bisabolol was thought to be restricted to Compositae plants. Here we reveal that alpha-bisabolol is also synthesized in rice, a non-Compositae plant, where it acts as a novel sesquiterpene phytoalexin. Overexpressing the gene responsible for the biosynthesis of alpha-bisabolol, OsTPS1, conferred bacterial blight resistance in rice. Phylogenomic analyses revealed that alpha-bisabolol-synthesizing enzymes in rice and Compositae evolved independently. Further experiments demonstrated that the natural variation in the disease resistance level was associated with differential transcription of OsTPS1 due to polymorphisms in its promoter. We demonstrated that OsTPS1 was regulated at the epigenetic level by JMJ705 through the methyl jasmonate pathway. These data reveal the cross-family accumulation and regulatory mechanisms of alpha-bisabolol production.

Genomic selection in tropical perennial crops and plantation trees: a review

To overcome the multiple challenges currently faced by agriculture, such as climate change and soil deterioration, more efficient plant breeding strategies are required. Genomic selection (GS) is crucial for the genetic improvement of quantitative traits, as it can increase selection intensity, shorten the generation interval, and improve selection accuracy for traits that are difficult to phenotype. Tropical perennial crops and plantation trees are of major economic importance and have consequently been the subject of many GS articles. In this review, we discuss the factors that affect GS accuracy (statistical models, linkage disequilibrium, information concerning markers, relatedness between training and target populations, the size of the training population, and trait heritability) and the genetic gain expected in these species. The impact of GS will be particularly strong in tropical perennial crops and plantation trees as they have long breeding cycles and constrained selection intensity. Future GS prospects are also discussed. High-throughput phenotyping will allow constructing of large training populations and implementing of phenomic selection. Optimized modeling is needed for longitudinal traits and multi-environment trials. The use of multi-omics, haploblocks, and structural variants will enable going beyond single-locus genotype data. Innovative statistical approaches, like artificial neural networks, are expected to efficiently handle the increasing amounts of heterogeneous multi-scale data. Targeted recombinations on sites identified from profiles of marker effects have the potential to further increase genetic gain. GS can also aid re-domestication and introgression breeding. Finally, GS consortia will play an important role in making the best of these opportunities.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01326-4.

How do plants defend themselves against pathogens-Biochemical mechanisms and genetic interventions

In agro-ecosystem, plant pathogens hamper food quality, crop yield, and global food security. Manipulation of naturally occurring defense mechanisms in host plants is an effective and sustainable approach for plant disease management. Various natural compounds, ranging from cell wall components to metabolic enzymes have been reported to protect plants from infection by pathogens and hence provide specific resistance to hosts against pathogens, termed as induced resistance. It involves various biochemical components, that play an important role in molecular and cellular signaling events occurring either before (elicitation) or after pathogen infection. The induction of reactive oxygen species, activation of defensive machinery of plants comprising of enzymatic and non-enzymatic antioxidative components, secondary metabolites, pathogenesis-related protein expression (e.g. chitinases and glucanases), phytoalexin production, modification in cell wall composition, melatonin production, carotenoids accumulation, and altered activity of polyamines are major induced changes in host plants during pathogen infection. Hence, the altered concentration of biochemical components in host plants restricts disease development. Such biochemical or metabolic markers can be harnessed for the development of "pathogen-proof" plants. Effective utilization of the key metabolites-based metabolic markers can pave the path for candidate gene identification. This present review discusses the valuable information for understanding the biochemical response mechanism of plants to cope with pathogens and genomics-metabolomics-based sustainable development of pathogen proof cultivars along with knowledge gaps and future perspectives to enhance sustainable agricultural production.

Preparation and Curation of Omics Data for Genome-Wide Association Studies

With the development of large-scale molecular phenotyping platforms, genome-wide association studies have greatly developed, being no longer limited to the analysis of classical agronomic traits, such as yield or flowering time, but also embracing the dissection of the genetic basis of molecular traits. Data generated by omics platforms, however, pose some technical and statistical challenges to the classical methodology and assumptions of an association study. Although genotyping data are subject to strict filtering procedures, and several advanced statistical approaches are now available to adjust for population structure, less attention has been instead devoted to the preparation of omics data prior to GWAS. In the present chapter, we briefly present the methods to acquire profiling data from transcripts, proteins, and small molecules, and discuss the tools and possibilities to clean, normalize, and remove the unwanted variation from large datasets of molecular phenotypic traits prior to their use in GWAS.

Rewilding crops for climate resilience: economic analysis and de novo domestication strategies

To match predicted population growth, annual food production should be doubled by 2050. This is not achievable by current agronomical and breeding practices, due to the impact of climate changes and associated abiotic stresses on agricultural production systems. Here, we analyze the impact of global climate trends on crop productivity and show that the overall loss in crop production from climate-driven abiotic stresses may exceed US$170 billion year-1 and represents a major threat to global food security. We also show that abiotic stress tolerance had been present in wild progenitors of modern crops but was lost during their domestication. We argue for a major shift in our paradigm of crop breeding, focusing on climate resilience, and call for a broader use of wild relatives as a major tool in this process. We argue that, while molecular tools are currently in place to harness the potential of climate-resilient genes present in wild relatives, the complex polygenic nature of tolerance traits remains a major bottleneck in this process. Future research efforts should be focused not only on finding appropriate wild relatives but also on development of efficient cell-based high-throughput phenotyping platforms allowing assessment of the in planta operation of key genes.

Effects of the Developmental Regulator BOLITA on the Plant Metabolome

Transcription factors are important regulators of gene expression. They can orchestrate the activation or repression of hundreds or thousands of genes and control diverse processes in a coordinated way. This work explores the effect of a master regulator of plant development, BOLITA (BOL), in plant metabolism, with a special focus on specialized metabolism. For this, we used an Arabidopsis thaliana line in which the transcription factor activity can be induced. Fingerprinting metabolomic analyses of whole plantlets were performed at different times after induction. After 96 h, all induced replicas clustered as a single group, in contrast with all controls which did not cluster. Metabolomic analyses of shoot and root tissues enabled the putative identification of differentially accumulated metabolites in each tissue. Finally, the analysis of global gene expression in induced vs. non-induced root samples, together with enrichment analyses, allowed the identification of enriched metabolic pathways among the differentially expressed genes and accumulated metabolites after the induction. We concluded that the induction of BOL activity can modify the Arabidopsis metabolome. Future work should investigate whether its action is direct or indirect, and the implications of the metabolic changes for development regulation and bioprospection.

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.

Tailoring Alginate/Chitosan Microparticles Loaded with Chemical and Biological Agents for Agricultural Application and Production of Value-Added Foods

This work reviews the recent development of biopolymer-based delivery systems for agricultural application. Encapsulation into biopolymer microparticles ensures the protection and targeted delivery of active agents while offering controlled release with higher efficiency and environmental safety for ecological and sustainable plant production. Encapsulation of biological agents provides protection and increases its survivability while providing an environment safe for growth. The application of microparticles loaded with chemical and biological agents presents an innovative way to stimulate plant metabolites synthesis. This enhances plants’ defense against pests and pathogens and results in the production of higher quality food (i.e., higher plant metabolites share). Ionic gelation was presented as a sustainable method in developing biopolymeric microparticles based on the next-generation biopolymers alginate and chitosan. Furthermore, this review highlights the advantages and disadvantages of advanced formulations against conventional ones. The significance of plant metabolites stimulation and their importance in functional food production is also pointed out. This review offers guidelines in developing biopolymeric microparticles loaded with chemical and biological agents and guidelines for the application in plant production, underlining its effect on the plant metabolites synthesis.

Metabolomics, a Powerful Tool for Understanding Plant Abiotic Stress

Metabolomics is a technology that generates large amounts of data and contributes to obtaining wide and integral explanations of the biochemical state of a living organism. Plants are continuously affected by abiotic stresses such as water scarcity, high temperatures and high salinity, and metabolomics has the potential for elucidating the response-to-stress mechanisms and develop resistance strategies in affected cultivars. This review describes the characteristics of each of the stages of metabolomic studies in plants and the role of metabolomics in the characterization of the response of various plant species to abiotic stresses.

Metabolomics Intervention Towards Better Understanding of Plant Traits

The majority of the most economically important plant and crop species are enriched with the availability of high-quality reference genome sequences forming the basis of gene discovery which control the important biochemical pathways. The transcriptomics and proteomics resources have also been made available for many of these plant species that intensify the understanding at expression levels. However, still we lack integrated studies spanning genomics–transcriptomics–proteomics, connected to metabolomics, the most complicated phase in phenotype expression. Nevertheless, for the past few decades, emphasis has been more on metabolome which plays a crucial role in defining the phenotype (trait) during crop improvement. The emergence of modern high throughput metabolome analyzing platforms have accelerated the discovery of a wide variety of biochemical types of metabolites and new pathways, also helped in improving the understanding of known existing pathways. Pinpointing the causal gene(s) and elucidation of metabolic pathways are very important for development of improved lines with high precision in crop breeding. Along with other-omics sciences, metabolomics studies have helped in characterization and annotation of a new gene(s) function. Hereby, we summarize several areas in the field of crop development where metabolomics studies have made its remarkable impact. We also assess the recent research on metabolomics, together with other omics, contributing toward genetic engineering to target traits and key pathway(s).

Integrating multi-omics data for crop improvement

Our agricultural systems are now in urgent need to secure food for a growing world population. To meet this challenge, we need a better characterization of plant genetic and phenotypic diversity. The combination of genomics, transcriptomics and metabolomics enables a deeper understanding of the mechanisms underlying the complex architecture of many phenotypic traits of agricultural relevance. We review the recent advances in plant genomics to see how these can be integrated with broad molecular profiling approaches to improve our understanding of plant phenotypic variation and inform crop breeding strategies.

Morphological and metabolic profiling of a tropical-adapted potato association panel subjected to water recovery treatment reveals new insights into plant vigor

Potato (Solanum tuberosum L.) is one of the world's most important crops, but it is facing major challenges due to climatic changes. To investigate the effects of intermittent drought on the natural variability of plant morphology and tuber metabolism in a novel potato association panel comprising 258 varieties we performed an augmented block design field study under normal irrigation and under water-deficit and recovery conditions in Ica, Peru. All potato genotypes were profiled for 45 morphological traits and 42 central metabolites via nuclear magnetic resonance. Statistical tests and norm of reaction analysis revealed that the observed variations were trait specific, that is, genotypic versus environmental. Principal component analysis showed a separation of samples as a result of conditional changes. To explore the relational ties between morphological traits and metabolites, correlation-based network analysis was employed, constructing one network for normal irrigation and one network for water-recovery samples. Community detection and difference network analysis highlighted the differences between the two networks, revealing a significant correlational link between fumarate and plant vigor. A genome-wide association study was performed for each metabolic trait. Eleven single nucleotide polymorphism (SNP) markers were associated with fumarate. Gene Ontology analysis of quantitative trait loci regions associated with fumarate revealed an enrichment of genes regulating metabolic processes. Three of the 11 SNPs were located within genes, coding for a protein of unknown function, a RING domain protein and a zinc finger protein ZAT2. Our findings have important implications for future potato breeding regimes, especially in countries suffering from climate change.

Towards model-driven characterization and manipulation of plant lipid metabolism

Plant lipids have versatile applications and provide essential fatty acids in human diet. Therefore, there has been a growing interest to better characterize the genetic basis, regulatory networks, and metabolic pathways that shape lipid quantity and composition. Addressing these issues is challenging due to context-specificity of lipid metabolism integrating environmental, developmental, and tissue-specific cues. Here we systematically review the known metabolic pathways and regulatory interactions that modulate the levels of storage lipids in oilseeds. We argue that the current understanding of lipid metabolism provides the basis for its study in the context of genome-wide plant metabolic networks with the help of approaches from constraint-based modeling and metabolic flux analysis. The focus is on providing a comprehensive summary of the state-of-the-art of modeling plant lipid metabolic pathways, which we then contrast with the existing modeling efforts in yeast and microalgae. We then point out the gaps in knowledge of lipid metabolism, and enumerate the recent advances of using genome-wide association and quantitative trait loci mapping studies to unravel the genetic regulations of lipid metabolism. Finally, we offer a perspective on how advances in the constraint-based modeling framework can propel further characterization of plant lipid metabolism and its rational manipulation.

A UV-B-responsive glycosyltransferase, OsUGT706C2, modulates flavonoid metabolism in rice

Although natural variations in rice flavonoids exist, and biochemical characterization of a few flavonoid glycosyltransferases has been reported, few studies focused on natural variations in tricin-lignan-glycosides and their underlying genetic basis. In this study, we carried out metabolic profiling of tricin-lignan-glycosides and identified a major quantitative gene annotated as a UDP-dependent glycosyltransferase OsUGT706C2 by metabolite-based genome-wide association analysis. The putative flavonoid glycosyltransferase OsUGT706C2 was characterized as a flavonoid 7-O-glycosyltransferas in vitro and in vivo. Although the in vitro enzyme activity of OsUGT706C2 was similar to that of OsUGT706D1, the expression pattern and induced expression profile of OsUGT706C2 were very different from those of OsUGT706D1. Besides, OsUGT706C2 was specifically induced by UV-B. Constitutive expression of OsUGT706C2 in rice may modulate phenylpropanoid metabolism at both the transcript and metabolite levels. Furthermore, overexpressing OsUGT706C2 can enhance UV-B tolerance by promoting ROS scavenging in rice. Our findings might make it possible to use the glycosyltransferase OsUGT706C2 for crop improvement with respect to UV-B adaptation and/or flavonoid accumulation, which may contribute to stable yield.

Co-Regulation of Clustered and Neo-Functionalized Genes in Plant-Specialized Metabolism

Current findings of neighboring genes involved in plant specialized metabolism provide the genomic signatures of metabolic evolution. Two such genomic features, namely, (i) metabolic gene cluster and (ii) neo-functionalization of tandem gene duplications, represent key factors corresponding to the creation of metabolic diversity of plant specialized metabolism. So far, several terpenoid and alkaloid biosynthetic genes have been characterized with gene clusters in some plants. On the other hand, some modification genes involved in flavonoid and glucosinolate biosynthesis were found to arise via gene neo-functionalization. Although the occurrence of both types of metabolic evolution are different, the neighboring genes are generally regulated by the same or related regulation factors. Therefore, the translation-based approaches associated with genomics, and transcriptomics are able to be employed for functional genomics focusing on plant secondary metabolism. Here, we present a survey of the current understanding of neighboring genes involved in plant secondary metabolism. Additionally, a genomic overview of neighboring genes of four model plants and transcriptional co-expression network neighboring genes to detect metabolic gene clusters in Arabidopsis is provided. Finally, the insights functional genomics have provided concerning the evolution and mechanistic regulation of both the formation and operation of metabolic neighboring clusters is discussed.

Bioinformatics Resources for Plant Abiotic Stress Responses: State of the Art and Opportunities in the Fast Evolving -Omics Era

Abiotic stresses are among the principal limiting factors for productivity in agriculture. In the current era of continuous climate changes, the understanding of the molecular aspects involved in abiotic stress response in plants is a priority. The rise of -omics approaches provides key strategies to promote effective research in the field, facilitating the investigations from reference models to an increasing number of species, tolerant and sensitive genotypes. Integrated multilevel approaches, based on molecular investigations at genomics, transcriptomics, proteomics and metabolomics levels, are now feasible, expanding the opportunities to clarify key molecular aspects involved in responses to abiotic stresses. To this aim, bioinformatics has become fundamental for data production, mining and integration, and necessary for extracting valuable information and for comparative efforts, paving the way to the modeling of the involved processes. We provide here an overview of bioinformatics resources for research on plant abiotic stresses, describing collections from -omics efforts in the field, ranging from raw data to complete databases or platforms, highlighting opportunities and still open challenges in abiotic stress research based on -omics technologies.

Metabolomics: A Way Forward for Crop Improvement

Metabolomics is an emerging branch of "omics" and it involves identification and quantification of metabolites and chemical footprints of cellular regulatory processes in different biological species. The metabolome is the total metabolite pool in an organism, which can be measured to characterize genetic or environmental variations. Metabolomics plays a significant role in exploring environment-gene interactions, mutant characterization, phenotyping, identification of biomarkers, and drug discovery. Metabolomics is a promising approach to decipher various metabolic networks that are linked with biotic and abiotic stress tolerance in plants. In this context, metabolomics-assisted breeding enables efficient screening for yield and stress tolerance of crops at the metabolic level. Advanced metabolomics analytical tools, like non-destructive nuclear magnetic resonance spectroscopy (NMR), liquid chromatography mass-spectroscopy (LC-MS), gas chromatography-mass spectrometry (GC-MS), high performance liquid chromatography (HPLC), and direct flow injection (DFI) mass spectrometry, have sped up metabolic profiling. Presently, integrating metabolomics with post-genomics tools has enabled efficient dissection of genetic and phenotypic association in crop plants. This review provides insight into the state-of-the-art plant metabolomics tools for crop improvement. Here, we describe the workflow of plant metabolomics research focusing on the elucidation of biotic and abiotic stress tolerance mechanisms in plants. Furthermore, the potential of metabolomics-assisted breeding for crop improvement and its future applications in speed breeding are also discussed. Mention has also been made of possible bottlenecks and future prospects of plant metabolomics.

Genetic Variation for Seed Metabolite Levels in Brachypodium distachyon

Metabolite composition and concentrations in seed grains are important traits of cereals. To identify the variation in the seed metabolotypes of a model grass, namely Brachypodium distachyon, we applied a widely targeted metabolome analysis to forty inbred lines of B. distachyon and examined the accumulation patterns of 183 compounds in the seeds. By comparing the metabolotypes with the population structure of these lines, we found signature metabolites that represent different accumulation patterns for each of the three B. distachyon subpopulations. Moreover, we found that thirty-seven metabolites exhibited significant differences in their accumulation between the lines Bd21 and Bd3-1. Using a recombinant inbred line (RIL) population from a cross between Bd3-1 and Bd21, we identified the quantitative trait loci (QTLs) linked with this variation in the accumulation of thirteen metabolites. Our metabolite QTL analysis illustrated that different genetic factors may presumably regulate the accumulation of 4-pyridoxate and pyridoxamine in vitamin B6 metabolism. Moreover, we found two QTLs on chromosomes 1 and 4 that affect the accumulation of an anthocyanin, chrysanthemin. These QTLs genetically interacted to regulate the accumulation of this compound. This study demonstrates the potential for metabolite QTL mapping in B. distachyon and provides new insights into the genetic dissection of metabolomic traits in temperate grasses.

De Novo Domestication: An Alternative Route toward New Crops for the Future

Current global agricultural production must feed over 7 billion people. However, productivity varies greatly across the globe and is under threat from both increased competitions for land and climate change and associated environmental deterioration. Moreover, the increase in human population size and dietary changes are putting an ever greater burden on agriculture. The majority of this burden is met by the cultivation of a very small number of species, largely in locations that differ from their origin of domestication. Recent technological advances have raised the possibility of de novo domestication of wild plants as a viable solution for designing ideal crops while maintaining food security and a more sustainable low-input agriculture. Here we discuss how the discovery of multiple key domestication genes alongside the development of technologies for accurate manipulation of several target genes simultaneously renders de novo domestication a route toward crops for the future.

Efficient Algorithms for Genomic Duplication Models

An important issue in evolutionary molecular biology is to discover genomic duplication episodes and their correspondence to the species tree. Existing approaches vary in the two fundamental aspects: the choice of evolutionary scenarios that model allowed locations of duplications in the species tree, and the rules of clustering gene duplications from gene trees into a single multiple duplication event. Here we study the method of clustering called minimum episodes for several models of allowed evolutionary scenarios with a focus on interval models in which every gene duplication has an interval consisting of allowed locations in the species tree. We present mathematical foundations for general genomic duplication problems. Next, we propose the first linear time and space algorithm for minimum episodes clustering jointly for any interval model and the algorithm for the most general model in which every evolutionary scenario is allowed. We also present a comparative study of different models of genomic duplication based on simulated and empirical datasets. We provided algorithms and tools that could be applied to solve efficiently minimum episodes clustering problems. Our comparative study helps to identify which model is the most reasonable choice in inferring genomic duplication events.

Global analysis of threonine metabolism genes unravel key players in rice to improve the abiotic stress tolerance

The diversity in plant metabolites with improved phytonutrients is essential to achieve global food security and sustainable crop yield. Our study using computational metabolomics genome wide association study (cmGWAS) reports on a comprehensive profiling of threonine (Thr) metabolite in rice. Sixteen abiotic stress responsive (AbSR) – Thr metabolite producing genes (ThrMPG), modulate metabolite levels and play a significant role determining both physiological and nutritional importance of rice. These AbSR-ThrMPG were computationally analysed for their protein properties using OryzaCyc through plant metabolic network analyser. A total of 1373 and 1028 SNPs were involved in complex traits and genomic variations. Comparative mapping of AbSR-ThrMPG revealed the chromosomal colinearity with C4 grass species. Further, computational expression pattern of these genes predicted a differential expression profiling in diverse developmental tissues. Protein interaction of protein coding gene sequences revealed that the abiotic stresses (AbS) are multigenic in nature. In silico expression of AbSR-ThrMPG determined the putative involvement in response to individual AbS. This is the first comprehensive genome wide study reporting on AbSR –ThrMPG analysis in rice. The results of this study provide a pivotal resource for further functional investigation of these key genes in the vital areas of manipulating AbS signaling in rice improvement.

Inferring duplication episodes from unrooted gene trees

Background

One of evolutionary molecular biology fundamental issues is to discover genomic duplication events and their correspondence to the species tree. Such events can be reconstructed by clustering single gene duplications inferred by reconciling a set of gene trees with a species tree.

Results

Here we propose the first solutions to the genomic duplication problem in which every reconciliation with the minimal number of single gene duplications is allowed and the method of clustering called minimum episodes under the assumption that input gene trees are unrooted.

Conclusions

We showed new theoretical properties of unrooted reconciliation for the duplication cost and apply them to design several exact and heuristic algorithms for solving the problem. Our evaluation study on empirical dataset confirmed several genomic duplication events from the literature and demonstrate that algorithms can be successfully applied.

Homeolog expression analysis in an allotriploid non-model crop via integration of transcriptomics and proteomics

The fate of doubled genes, from allopolyploid or autopolyploid origin, is controlled at multiple levels, resulting in the modern day cultivars. We studied the root growth of 3 different triploid banana cultivars under control and osmotic stress conditions. The root growth of the allopolyploid ABB cultivar was 42% higher under control and 61% higher under osmotic stress. By integrating transcriptomics and proteomics, we studied the gene expression of all 3 cultivars, resulting in 2,749 identified root proteins. 383 gene loci displayed genotype specific differential expression whereof 252 showed at least one Single Amino Acid Polymorphism (SAAP). In the ABB cultivar, allele expressions supposedly follow a 1/3 and 2/3 pattern for respectively the A and the B allele. Using transcriptome read alignment to assess the homeoallelic contribution we found that 63% of the allele specific genes deviated from this expectation. 32 gene loci even did not express the A allele. The identified ABB allele- specific proteins correlate well with the observed growth phenotype as they are enriched in energy related functions such as ATP metabolic processes, nicotinamide nucleotide metabolic processes, and glycolysis.

Differentially evolved glucosyltransferases determine natural variation of rice flavone accumulation and UV-tolerance

Decoration of phytochemicals contributes to the majority of metabolic diversity in nature, whereas how this process alters the biological functions of their precursor molecules remains to be investigated. Flavones, an important yet overlooked subclass of flavonoids, are most commonly conjugated with sugar moieties by UDP-dependent glycosyltransferases (UGTs). Here, we report that the natural variation of rice flavones is mainly determined by OsUGT706D1 (flavone 7-O-glucosyltransferase) and OsUGT707A2 (flavone 5-O-glucosyltransferase). UV-B exposure and transgenic evaluation demonstrate that their allelic variation contributes to UV-B tolerance in nature. Biochemical characterization of over 40 flavonoid UGTs reveals their differential evolution in angiosperms. These combined data provide biochemical insight and genetic regulation into flavone biosynthesis and additionally suggest that adoption of the positive alleles of these genes into breeding programs will likely represent a potential strategy aimed at producing stress-tolerant plants.

The Genetics of Plant Metabolism

Plant metabolic studies have traditionally focused on the role and regulation of the enzymes catalyzing key reactions within specific pathways. Within the past 20 years, reverse genetic approaches have allowed direct determination of the effects of the deficiency, or surplus, of a given protein on the biochemistry of a plant. In parallel, top-down approaches have also been taken, which rely on screening broad, natural genetic diversity for metabolic diversity. Here, we compare and contrast the various strategies that have been adopted to enhance our understanding of the natural diversity of metabolism. We also detail how these approaches have enhanced our understanding of both specific and global aspects of the genetic regulation of metabolism. Finally, we discuss how such approaches are providing important insights into the evolution of plant secondary metabolism.

Quantitatively Understanding Plant Signaling: Novel Theoretical-Experimental Approaches

With the need to respond to and integrate a multitude of external and internal stimuli, plant signaling is highly complex, exhibiting signaling component redundancy and high interconnectedness between individual pathways. We review here novel theoretical-experimental approaches in manipulating plant signaling towards the goal of a comprehensive understanding and targeted quantitative control of plant processes. We highlight approaches taken in the field of synthetic biology used in other systems and discuss their applicability in plants. Finally, we introduce existing tools for the quantitative analysis and monitoring of plant signaling and the integration of experimentally obtained quantitative data into mathematical models. Incorporating principles of synthetic biology into plant sciences more widely will lead this field forward in both fundamental and applied research.

Current understanding of the pathways of flavonoid biosynthesis in model and crop plants

Flavonoids are a signature class of secondary metabolites formed from a relatively simple collection of scaffolds. They are extensively decorated by chemical reactions including glycosylation, methylation, and acylation. They are present in a wide variety of fruits and vegetables and as such in Western populations it is estimated that 20-50 mg of flavonoids are consumed daily per person. In planta they have demonstrated to contribute to both flower color and UV protection. Their consumption has been suggested to presenta wide range of health benefits. Recent technical advances allowing affordable whole genome sequencing, as well as a better inventory of species-by-species chemical diversity, have greatly advanced our understanding as to how flavonoid biosynthesis pathways vary across species. In parallel, reverse genetics combined with detailed molecular phenotyping is currently allowing us to elucidate the functional importance of individual genes and metabolites and by this means to provide further mechanistic insight into their biological roles. Here we provide an inventory of current knowledge of pathways of flavonoid biosynthesis in both the model plant Arabidopsis thaliana and a range of crop species, including tomato, maize, rice, and bean.

Ageratum enation virus Infection Induces Programmed Cell Death and Alters Metabolite Biosynthesis in Papaver somniferum

A previously unknown disease which causes severe vein thickening and inward leaf curl was observed in a number of opium poppy (Papaver somniferum L.) plants. The sequence analysis of full-length viral genome and associated betasatellite reveals the occurrence of Ageratum enation virus (AEV) and Ageratum leaf curl betasatellite (ALCB), respectively. Co-infiltration of cloned agroinfectious DNAs of AEV and ALCB induces the leaf curl and vein thickening symptoms as were observed naturally. Infectivity assay confirmed this complex as the cause of disease and also satisfied the Koch's postulates. Comprehensive microscopic analysis of infiltrated plants reveals severe structural anomalies in leaf and stem tissues represented by unorganized cell architecture and vascular bundles. Moreover, the characteristic blebs and membranous vesicles formed due to the virus-induced disintegration of the plasma membrane and intracellular organelles were also present. An accelerated nuclear DNA fragmentation was observed by Comet assay and confirmed by TUNEL and Hoechst dye staining assays suggesting virus-induced programmed cell death. Virus-infection altered the biosynthesis of several important metabolites. The biosynthesis potential of morphine, thebaine, codeine, and papaverine alkaloids reduced significantly in infected plants except for noscapine whose biosynthesis was comparatively enhanced. The expression analysis of corresponding alkaloid pathway genes by real time-PCR corroborated well with the results of HPLC analysis for alkaloid perturbations. The changes in the metabolite and alkaloid contents affect the commercial value of the poppy plants.

Omic Relief for the Biotically Stressed: Metabolomics of Plant Biotic Interactions

Many aspects of the way plants protect themselves against pathogen attack, or react upon such an attack, are realized by metabolites. The ambitious aim of metabolomics, namely the identification and annotation of the entire cellular metabolome, still poses a considerable challenge due to the high diversity of the metabolites in the cell. Recent advances in analytical methods and data analysis have resulted in improved sensitivity, accuracy, and capacity, allowing the analysis of several hundreds or even thousands of compounds within one sample. Investigators have only recently begun to acknowledge and harness the power of metabolomics to elucidate key questions in the study of plant biotic interactions; we review trends and developments in the field.

Plant Metabolomics: An Indispensable System Biology Tool for Plant Science

As genomes of many plant species have been sequenced, demand for functional genomics has dramatically accelerated the improvement of other omics including metabolomics. Despite a large amount of metabolites still remaining to be identified, metabolomics has contributed significantly not only to the understanding of plant physiology and biology from the view of small chemical molecules that reflect the end point of biological activities, but also in past decades to the attempts to improve plant behavior under both normal and stressed conditions. Hereby, we summarize the current knowledge on the genetic and biochemical mechanisms underlying plant growth, development, and stress responses, focusing further on the contributions of metabolomics to practical applications in crop quality improvement and food safety assessment, as well as plant metabolic engineering. We also highlight the current challenges and future perspectives in this inspiring area, with the aim to stimulate further studies leading to better crop improvement of yield and quality.

A Belated Green Revolution for Cannabis: Virtual Genetic Resources to Fast-Track Cultivar Development

Cannabis is a predominantly diecious phenotypically diverse domesticated genus with few if any extant natural populations. International narcotics conventions and associated legislation have constrained the establishment, characterization, and use of Cannabis genetic resource collections. This has resulted in the underutilization of genepool variability in cultivar development and has limited the inclusion of secondary genepools associated with genetic improvement strategies of the Green Revolution. The structured screening of ex situ germplasm and the exploitation of locally-adapted intraspecific traits is expected to facilitate the genetic improvement of Cannabis. However, limited attempts have been made to establish the full extent of genetic resources available for pre-breeding. We present a thorough critical review of Cannabis ex situ genetic resources, and discuss recommendations for conservation, pre-breeding characterization, and genetic analysis that will underpin future cultivar development. We consider East Asian germplasm to be a priority for conservation based on the prolonged historical cultivation of Cannabis in this region over a range of latitudes, along with the apparent high levels of genetic diversity and relatively low representation in published genetic resource collections. Seed cryopreservation could improve conservation by reducing hybridization and genetic drift that may occur during Cannabis germplasm regeneration. Given the unique legal status of Cannabis, we propose the establishment of a global virtual core collection based on the collation of consistent and comprehensive provenance meta-data and the adoption of high-throughput DNA sequencing technologies. This would enable representative core collections to be used for systematic phenotyping, and so underpin breeding strategies for the genetic improvement of Cannabis.

A high-density, multi-parental SNP genetic map on apple validates a new mapping approach for outcrossing species

Quantitative trait loci (QTL) mapping approaches rely on the correct ordering of molecular markers along the chromosomes, which can be obtained from genetic linkage maps or a reference genome sequence. For apple (Malus domestica Borkh), the genome sequence v1 and v2 could not meet this need; therefore, a novel approach was devised to develop a dense genetic linkage map, providing the most reliable marker-loci order for the highest possible number of markers. The approach was based on four strategies: (i) the use of multiple full-sib families, (ii) the reduction of missing information through the use of HaploBlocks and alternative calling procedures for single-nucleotide polymorphism (SNP) markers, (iii) the construction of a single backcross-type data set including all families, and (iv) a two-step map generation procedure based on the sequential inclusion of markers. The map comprises 15 417 SNP markers, clustered in 3 K HaploBlock markers spanning 1 267 cM, with an average distance between adjacent markers of 0.37 cM and a maximum distance of 3.29 cM. Moreover, chromosome 5 was oriented according to its homoeologous chromosome 10. This map was useful to improve the apple genome sequence, design the Axiom Apple 480 K SNP array and perform multifamily-based QTL studies. Its collinearity with the genome sequences v1 and v3 are reported. To our knowledge, this is the shortest published SNP map in apple, while including the largest number of markers, families and individuals. This result validates our methodology, proving its value for the construction of integrated linkage maps for any outbreeding species.

Correlation-Based Network Analysis of Metabolite and Enzyme Profiles Reveals a Role of Citrate Biosynthesis in Modulating N and C Metabolism in Zea mays

To investigate the natural variability of leaf metabolism and enzymatic activity in a maize inbred population, statistical and network analyses were employed on metabolite and enzyme profiles. The test of coefficient of variation showed that sugars and amino acids displayed opposite trends in their variance within the population, consistently with their related enzymes. The overall higher CV values for metabolites as compared to the tested enzymes are indicative for their greater phenotypic plasticity. H(2) tests revealed galactinol (1) and asparagine (0.91) as the highest scorers among metabolites and nitrate reductase (0.73), NAD-glutamate dehydrogenase (0.52), and phosphoglucomutase (0.51) among enzymes. The overall low H(2) scores for metabolites and enzymes are suggestive for a great environmental impact or gene-environment interaction. Correlation-based network generation followed by community detection analysis, partitioned the network into three main communities and one dyad, (i) reflecting the different levels of phenotypic plasticity of the two molecular classes as observed for the CV values and (ii) highlighting the concerted changes between classes of chemically related metabolites. Community 1 is composed mainly of enzymes and specialized metabolites, community 2' is enriched in N-containing compounds and phosphorylated-intermediates. The third community contains mainly organic acids and sugars. Cross-community linkages are supported by aspartate, by the photorespiration amino acids glycine and serine, by the metabolically related GABA and putrescine, and by citrate. The latter displayed the strongest node-betweenness value (185.25) of all nodes highlighting its fundamental structural role in the connectivity of the network by linking between different communities and to the also strongly connected enzyme aldolase.

Integrative Approaches to Enhance Understanding of Plant Metabolic Pathway Structure and Regulation

Huge insight into molecular mechanisms and biological network coordination have been achieved following the application of various profiling technologies. Our knowledge of how the different molecular entities of the cell interact with one another suggests that, nevertheless, integration of data from different techniques could drive a more comprehensive understanding of the data emanating from different techniques. Here, we provide an overview of how such data integration is being used to aid the understanding of metabolic pathway structure and regulation. We choose to focus on the pairwise integration of large-scale metabolite data with that of the transcriptomic, proteomics, whole-genome sequence, growth- and yield-associated phenotypes, and archival functional genomic data sets. In doing so, we attempt to provide an update on approaches that integrate data obtained at different levels to reach a better understanding of either single gene function or metabolic pathway structure and regulation within the context of a broader biological process.