C. Eduardo Vallejos

Common bean (Phaseolus vulgaris), a staple food in Latin America and Africa, serves as a vital source of energy, protein, and essential minerals for millions of people. However, genomics knowledge that breeders could leverage for improvement of this crop is scarce. We have developed and validated a comparative genomics approach to predict conserved transcription factor binding sites (TFBS) in common bean and studied gene regulatory networks. We analyzed promoter regions and identified TFBS for 12,631 bean genes with an average of 6 conserved motifs per gene. Moreover, we discovered a statistically significant relationship between the number of conserved motifs and amount of available experimental evidence of gene regulation. Notably, ERF, MYB, and bHLH transcription factor families dominated conserved motifs, with implications for starch biosynthesis regulation. Furthermore, we provide gene regulatory data as a resource that can be interrogated for the regulatory landscape of any set of genes. Our results underscore the significance of TFBS conservation in legumes and aligns with the notion that core genes often exhibit a more conserved regulatory makeup. The study demonstrates the effectiveness of a comparative genomics approach for addressing genome information gaps in non-model organisms and provides valuable insights into the regulatory networks governing starch biosynthesis genes that can support crop improvement programs.

Plants have evolved various photosynthetic adaptations and photoprotective mechanisms to survive in fluctuating and extreme light environments. Many light-activated photosynthetic proteins and enzymes adjust to plant leaf anatomy and leaf pigments to facilitate these processes. Under excessive amounts of light, plants use non-photochemical quenching (NPQ) mechanisms to dissipate excess absorbed light energy as heat to prevent photoinhibition and, therefore, mitigate damage to the plant’s photosystems. In this study, we examined photosynthetic adaptations to the light environment in common beans using representative genotypes of the Andean (Calima) and the Mesoamerican (Jamapa) gene pools. We estimated their leaf chlorophyll fluorescence characteristics using dark- and light-adapted mature leaves from three-week-old plants. Our results indicated a higher chlorophyll fluorescence of the light-adapted leaves in the Mesoamerican genotype. NPQ induction was early and extended in the Andean genotype. A similar response in the Mesoamerican counterpart required high light intensity (≥1500 PAR). The NPQ relaxation was rapid in the Mesoamerican genotype (t_1/2: 6.76 min) but sluggish in the Andean genotype (t_1/2: 9.17 min). These results indicated variable adaptation to light environments between the two common bean genotypes and suggested different strategies for surviving fluctuating light environments that can be exploited for developing plants with environmentally efficient photosynthesis under light limitations.

The efficiency of CO ₂ flux in the leaf is hindered by a several structural and biochemical barriers which affect the overall net photosynthesis. However, the dearth of information about the genetic control of these features is limiting our ability for genetic manipulation. We performed a comparative analysis between a Mesoamerican and an Andean cultivar of Phaseolus vulgaris at variable light and CO ₂ levels. The Mesoamerican bean had higher photosynthetic rate, maximum rate of rubisco carboxylase activity and maximum rate of photosynthetic electron transport at light saturation conditions than its Andean counterpart. Leaf anatomy comparison between genotypes showed that the Mesoamerican bean had smaller cell sizes than the Andean bean. Smaller epidermal cells in the Mesoamerican bean resulted in higher stomata density and consequently higher stomatal conductance for water vapor and CO ₂ than in the Andean bean. Likewise, smaller palisade and spongy mesophyll cells in the Mesoamerican than in the Andean bean increased the cell surface area per unit of volume and consequently increased mesophyll conductance. Finally, smaller cells in the Mesoamerican also increased chlorophyll and protein concentration per unit of leaf area. In summary, we show that differential cell size controls the overall net photosynthesis and could be used as a target for genetic manipulation to improve photosynthesis. Highlight: PhotosyntheUc performance comparison between a Mesoamerican and an Andean bean genotype showed higher rate at increased light and CO ₂ levels. Differences could be explained by variaUon in cell size.

Bacterial spot caused by Xanthomonas euvesicatoria is a major disease of pepper ( Capsicum annuum L.) in warm and humid production environments. Use of genetically resistant cultivars is an effective approach to manage bacterial spot. Two recessive resistance genes, bs5 and bs6 , confer non-race-specific resistance against bacterial spot. The objective of our study was to map these two loci in the pepper genome. We used a genotyping-by-sequencing approach to initially map the position of the two resistances. Segregant populations for bs5 and bs6 were developed by crossing susceptible Early CalWonder (ECW) with near-isogenic lines ECW50R ( bs5 introgression) or ECW60R ( bs6 introgression). Following fine-mapping, bs5 was delimited to a ~535 Kbp interval on chromosome 3, and bs6 to a ~666 Kbp interval in chromosome 6 of pepper. We also identified 14 and 8 candidate resistance genes for bs5 and bs6 , respectively, based on predicted protein coding polymorphisms between ECW and the corresponding resistant parent. Mapping of bs5 and bs6 will facilitate their use in breeding programs through marker-assisted selection and is also a crucial step towards understanding the mechanisms of resistance. Key message: Two recessive bacterial spot resistance genes were mapped in the pepper genome, which will facilitate their advancement in commercial pepper for management of all races of Xanthomonas euvesicatoria .

Background: Predicting the phenotype from the genotype is one of the major contemporary challenges in biology. This challenge is greater in plants because their development occurs mostly post-embryonically under diurnal and seasonal environmental fluctuations. Most current crop simulation models are physiology-based models capable of capturing environmental fluctuations but cannot adequately capture genotypic effects because they were not constructed within a genetics framework. Results: We describe the construction of a mixed-effects dynamic model to predict time-to-flowering in the common bean (Phaseolus vulgaris L.). This prediction model applies the developmental approach used by traditional crop simulation models, uses direct observational data, and captures the Genotype, Environment, and Genotype-by-Environment effects to predict progress towards time-to-flowering in real time. Comparisons to a traditional crop simulation model and to a previously developed static model shows the advantages of the new dynamic model. Conclusions: The dynamic model can be applied to other species and to different plant processes. These types of models can, in modular form, gradually replace plant processes in existing crop models as has been implemented in BeanGro, a crop simulation model within the DSSAT Cropping Systems Model. Gene-based dynamic models can accelerate precision breeding of diverse crop species, particularly with the prospects of climate change. Finally, a gene-based simulation model can assist policy decision makers in matters pertaining to prediction of food supplies.

BackgroundPredicting the phenotype from the genotype is one of the major contemporary challenges in biology. This challenge is greater in plants because their development occurs mostly post-embryonically under diurnal and seasonal environmental fluctuations. Most current crop simulation models are physiology-based models capable of capturing environmental fluctuations but cannot adequately capture genotypic effects because they were not constructed within a genetics framework. ResultsWe describe the construction of a mixed-effects dynamic model to predict time-to-flowering in the common bean (Phaseolus vulgaris L.). This prediction model applies the developmental approach used by traditional crop simulation models, uses direct observational data, and captures the Genotype, Environment, and Genotype-by-Environment effects to predict progress towards time-to-flowering in real time. Comparisons to a traditional crop simulation model and to a previously developed static model shows the advantages of the new dynamic model.ConclusionsThe dynamic model can be applied to other species and to different plant processes. These types of models can, in modular form, gradually replace plant processes in existing crop models as has been implemented in BeanGro, a crop simulation model within the DSSAT Cropping Systems Model. Gene-based dynamic models can accelerate precision breeding of diverse crop species, particularly with the prospects of climate change. Finally, a gene-based simulation model can assist policy decision makers in matters pertaining to prediction of food supplies.

Predicting the phenotype from the genotype is one of the major contemporary challenges in biology. This challenge is greater in plants because their development occurs mostly post-embryonically under diurnal and seasonal environmental fluctuations. Current phenotype prediction models do not adequately capture all of these fluctuations or effectively use genotype information. Instead, we have developed a dynamic modular approach that captures the genotype, environment, and Genotype-by-Environment effects to express the time-to-flowering phenotype in real time in Phaseolus vulgaris . The module we describe can be applied to different plant processes and can gradually replace processes in existing crop models. Our model can enable accelerated progress in diverse breeding programs, particularly with the prospects of climate change. Finally, a gene-based simulation model can assist policy decision makers in matters pertaining to prediction of food supplies.

Predicting the consequences of manipulating genotype (G) and agronomic management (M) on agricultural ecosystem performances under future environmental (E) conditions remains a challenge. Crop modelling has the potential to enable society to assess the efficacy of G × M technologies to mitigate and adapt crop production systems to climate change. Despite recent achievements, dedicated research to develop and improve modelling capabilities from gene to global scales is needed to provide guidance on designing G × M adaptation strategies with full consideration of their impacts on both crop productivity and ecosystem sustainability under varying climatic conditions. Opportunities to advance the multiscale crop modelling framework include representing crop genetic traits, interfacing crop models with large-scale models, improving the representation of physiological responses to climate change and management practices, closing data gaps and harnessing multisource data to improve model predictability and enable identification of emergent relationships. A fundamental challenge in multiscale prediction is the balance between process details required to assess the intervention and predictability of the system at the scales feasible to measure the impact. An advanced multiscale crop modelling framework will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales.

Background: To maximize photosynthetic efficiency, plants have evolved a capacity by which leaf area scales allometrically with leaf mass through interactions with the environment. However, our understanding of genetic control of this allometric relationship remains limited.Results: We integrated allometric scaling laws expressed at static and ontogenetic levels into genetic mapping to identify the quantitative trait loci (QTLs) that mediate how leaf area scales with leaf mass and how such leaf allometry, under the control of these QTLs, varies as a response to environment change. A major QTL detected by the static model constantly affects the allometric growth of leaf area vs. leaf mass for the common bean (Phaseolus vulgaris) in two different environments. The ontogenetic model identified this QTL plus a few other QTLs that determine developmental trajectories of leaf allometry, whose expression is contingent heavily upon the environment.Conclusions: Our results gain new insight into the genetic mechanisms of how plants program their leaf morphogenesis to adapt to environmental perturbations.