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Maize (Zea mays) production systems are heavily reliant on the provision of managed inputs such as fertilizers to maximize growth and yield. Hence, the effective use of nitrogen (N) fertilizer is crucial to minimize the associated financial and environmental costs, as well as maximize yield. However, how to effectively utilize N inputs for increased grain yields remains a substantial challenge for maize growers that requires a deeper understanding of the underlying physiological responses to N fertilizer application. We report a multiscale investigation of five field-grown maize hybrids under low or high N supplementation regimes that includes the quantification of phenolic and prenyl-lipid compounds, cellular ultrastructural features, and gene expression traits at three developmental stages of growth. Our results reveal that maize perceives the lack of supplemented N as a stress and, when provided with additional N, will prolong vegetative growth. However, the manifestation of the stress and responses to N supplementation are highly hybrid-specific. Eight genes were differentially expressed in leaves in response to N supplementation in all tested hybrids and at all developmental stages. These genes represent potential biomarkers of N status and include two isoforms of Thiamine Thiazole Synthase involved in vitamin B1 biosynthesis. Our results uncover a detailed view of the physiological responses of maize hybrids to N supplementation in field conditions that provides insight into the interactions between management practices and the genetic diversity within maize.

Domestication of cranberry and blueberry began in the United States in the early 1800s and 1900s, respectively, and in part owing to their flavors and health-promoting benefits are now cultivated and consumed worldwide. The industry continues to face a wide variety of production challenges (e.g. disease pressures), as well as a demand for higher-yielding cultivars with improved fruit quality characteristics. Unfortunately, molecular tools to help guide breeding efforts for these species have been relatively limited compared with those for other high-value crops. Here, we describe the construction and analysis of the first pangenome for both blueberry and cranberry. Our analysis of these pangenomes revealed both crops exhibit great genetic diversity, including the presence–absence variation of 48.4% genes in highbush blueberry and 47.0% genes in cranberry. Auxiliary genes, those not shared by all cultivars, are significantly enriched with molecular functions associated with disease resistance and the biosynthesis of specialized metabolites, including compounds previously associated with improving fruit quality traits. The discovery of thousands of genes, not present in the previous reference genomes for blueberry and cranberry, will serve as the basis of future research and as potential targets for future breeding efforts. The pangenome, as a multiple-sequence alignment, as well as individual annotated genomes, are publicly available for analysis on the Genome Database for Vaccinium—a curated and integrated web-based relational database. Lastly, the core-gene predictions from the pangenomes will serve useful to develop a community genotyping platform to guide future molecular breeding efforts across the family.

13C‐Metabolic Flux Analysis (13C‐MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both methods use metabolic reaction network models of metabolism operating at steady state so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. These fluxes can shed light on basic biology and have been successfully used to inform metabolic engineering strategies. Several approaches have been taken to test the reliability of estimates and predictions from constraint‐based methods and to compare alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, such as the quantification of flux estimate uncertainty, validation and model selection methods have been underappreciated and underexplored. We review the history and state‐of‐the‐art in constraint‐based metabolic model validation and model selection. Applications and limitations of the χ²‐test of goodness‐of‐fit, the most widely used quantitative validation and selection approach in 13C‐MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C‐MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how adopting robust validation and selection procedures can enhance confidence in constraint‐based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology.

Anthracnose fruit rot (AFR), caused by the fungal pathogen Colletotrichum fioriniae, is among the most destructive and widespread fruit disease of blueberry, impacting both yield and overall fruit quality. Blueberry cultivars have highly variable resistance against AFR. To date, this pathogen is largely controlled by applying various fungicides; thus, a more cost-effective and environmentally conscious solution for AFR is needed. Here we report three quantitative trait loci associated with AFR resistance in northern highbush blueberry (Vaccinium corymbosum). Candidate genes within these genomic regions are associated with the biosynthesis of flavonoids (e.g. anthocyanins) and resistance against pathogens. Furthermore, we examined gene expression changes in fruits following inoculation with Colletotrichum in a resistant cultivar, which revealed an enrichment of significantly differentially expressed genes associated with certain specialized metabolic pathways (e.g. flavonol biosynthesis) and pathogen resistance. Using non-targeted metabolite profiling, we identified a flavonol glycoside with properties consistent with a quercetin rhamnoside as a compound exhibiting significant abundance differences among the most resistant and susceptible individuals from the genetic mapping population. Further analysis revealed that this compound exhibits significant abundance differences among the most resistant and susceptible individuals when analyzed as two groups. However, individuals within each group displayed considerable overlapping variation in this compound, suggesting that its abundance may only be partially associated with resistance against C. fioriniae. These findings should serve as a powerful resource that will enable breeding programs to more easily develop new cultivars with superior resistance to AFR and as the basis of future research studies.

Differential gene expression between environments often underlies phenotypic plasticity. However, environment-specific expression patterns are hypothesized to relax selection on genes, and thus limit plasticity evolution. We collated over 27 terabases of RNA-sequencing data on Arabidopsis thaliana from over 300 peer-reviewed studies and 200 treatment conditions to investigate this hypothesis. Consistent with relaxed selection, genes with more treatment-specific expression have higher levels of nucleotide diversity and divergence at nonsynonymous sites but lack stronger signals of positive selection. This result persisted even after controlling for expression level, gene length, GC content, the tissue specificity of expression, and technical variation between studies. Overall, our investigation supports the existence of a hypothesized trade-off between the environment specificity of a gene’s expression and the strength of selection on said gene in A. thaliana. Future studies should leverage multiple genome-scale datasets to tease apart the contributions of many variables in limiting plasticity evolution.

The modeling of rates of biochemical reactions—fluxes—in metabolic networks is widely used for both basic biological research and biotechnological applications. A number of different modeling methods have been developed to estimate and predict fluxes, including kinetic and constraint‐based (Metabolic Flux Analysis and flux balance analysis) approaches. Although different resources exist for teaching these methods individually, to‐date no resources have been developed to teach these approaches in an integrative way that equips learners with an understanding of each modeling paradigm, how they relate to one another, and the information that can be gleaned from each. We have developed a series of modeling simulations in Python to teach kinetic modeling, metabolic control analysis, 13C‐metabolic flux analysis, and flux balance analysis. These simulations are presented in a series of interactive notebooks with guided lesson plans and associated lecture notes. Learners assimilate key principles using models of simple metabolic networks by running simulations, generating and using data, and making and validating predictions about the effects of modifying model parameters. We used these simulations as the hands‐on computer laboratory component of a four‐day metabolic modeling workshop and participant survey results showed improvements in learners' self‐assessed competence and confidence in understanding and applying metabolic modeling techniques after having attended the workshop. The resources provided can be incorporated in their entirety or individually into courses and workshops on bioengineering and metabolic modeling at the undergraduate, graduate, or postgraduate level.

Dry beans (Phaseolus vulgarisL.) are a nutritious food, but their lengthy cooking requirements are barriers to consumption. Presoaking is one strategy to reduce cooking time. Soaking allows hydration to occur prior to cooking, and enzymatic changes to pectic polysaccharides also occur during soaking that shorten the cooking time of beans. Little is known about how gene expression during soaking influences cooking times. The objectives of this study were to (1) identify gene expression patterns that are altered by soaking and (2) compare gene expression in fast‐cooking and slow‐cooking bean genotypes. RNA was extracted from four bean genotypes at five soaking time points (0, 3, 6, 12, and 18 h) and expression abundances were detected using Quant‐seq. Differential gene expression analysis and weighted gene coexpression network analysis were used to identify candidate genes within quantitative trait loci for water uptake and cooking time. Genes related to cell wall growth and development as well as hypoxic stress were differentially expressed between the fast‐ and slow‐cooking beans due to soaking. Candidate genes identified in the slow‐cooking beans included enzymes that increase intracellular calcium concentrations and cell wall modification enzymes. The expression of cell wall‐strengthening enzymes in the slow‐cooking beans may increase their cooking time and ability to resist osmotic stress by preventing cell separation and water uptake in the cotyledon.

Tepary bean (Phaseolus acutifoliusA. Gray), indigenous to the arid climates of northern Mexico and the Southwest United States, diverged from common bean (Phaseolus vulgarisL.), approximately 2 million years ago and exhibits a wide range of resistance to biotic stressors. The tepary genome is highly syntenic to the common bean genome providing a foundation for discovery and breeding of agronomic traits between these two crop species. Although a limited number of adaptive traits from tepary bean have been introgressed into common bean, hybridization barriers between these two species required the development of bridging lines to alleviate this barrier. Thus, to fully utilize the extant tepary bean germplasm as both a crop and as a donor of adaptive traits, we developed a diversity panel of 422 cultivated, weedy, and wild tepary bean accessions which were then genotyped and phenotyped to enable population genetic analyses and genome‐wide association studies for their response to a range of biotic stressors. Population structure analyses of the panel revealed eight subpopulations and the differentiation of botanical varieties withinP. acutifolius. Genome‐wide association studies revealed loci and candidate genes underlying biotic stress resistance including quantitative trait loci for resistance to weevils, common bacterial blight, Fusarium wilt, and bean common mosaic necrosis virus that can be harnessed not only for tepary bean but also common bean improvement.

Common bean (Phaseolus vulgarisL.) is a nutrient-rich food, but its long cooking times hinder its wider utilization. The Yellow Bean Collection (YBC) was assembled with 295 genotypes from global sources to assess the genetic and phenotypic diversity for end-use quality traits in yellow beans. The panel was genotyped with over 2,000 SNPs identified via Genotyping-By-Sequencing (GBS). Through population structure analyses with the GBS markers, the YBC was determined to be 69% Andean, 26% Middle American, and 5% admixture. The YBC was grown in two major bean production regions in the U.S., Michigan (MI) and Nebraska (NE) over two years. The genotypes exhibited a wide diversity in days to flower, seed weight, water uptake, and cooking time. The cooking times of the YBC ranged from 17–123 min. The cooking time were longer and varied more widely in NE with many more genotypes exhibiting hardshell than in MI. Fast-cooking genotypes were identified with various yellow colors; 20 genotypes cooked within 20 min in MI, and eight genotypes cooked within 31 min in NE. Water uptake and cooking time were significantly affected by the environment, which included both the growing and cooking environment, and notably in relation to cooking, NE is higher elevation than MI. SNPs associated with cooking time were identified with genome-wide association analyses and a polygalacturonase gene on Pv04 was considered to be a candidate gene. The genotypic and phenotypic variability, fast-cooking genotypes, and the associated SNPs of the YBC will lay the foundation for utilizing yellow beans for breeding and genetic analyses.