Biology subjects

Vallejos, C. E.

Publications and source records attributed to Vallejos, C. E..

4 recordsLinked to original sources

bioRxiv2024

Cell Size Controls Photosynthetic Capacity in a Mesoamerican and an Andean Genotype of Phaseolus vulgaris L.

The efficiency of CO2 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 CO2 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 CO2 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. HighlightPhotosyntheUc performance comparison between a Mesoamerican and an Andean bean genotype showed higher rate at increased light and CO2 levels. Differences could be explained by variaUon in cell size.

physiology↗

bioRxiv2022

Mapping of the bs5 and bs6 non-race-specific recessive resistances against bacterial spot of pepper

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 messageTwo 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.

plant biology↗

bioRxiv2021

Dynamic Gene-Based Ecophysiological Models to Predict Phenotype from Genotype and Environment Data

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.

plant biology↗

bioRxiv2021

INCORPORATING A DYNAMIC GENE-BASED PROCESS MODULE INTO A CROP SIMULATION MODEL

Dynamic crop simulation models are tools that predict plant phenotype grown in specific environments for genotypes using genotype-specific parameters (GSPs), often referred to as "genetic coefficients." These GSPs are estimated using phenotypic observations and may not represent "true" genetic information. Instead, estimating GSPs requires experiments to measure phenotypic responses when new cultivars are released. The goal of this study was to evaluate a new approach that incorporates a dynamic gene-based module for simulating time-to-flowering for common bean (Phaseolus vulgaris L.) into an existing dynamic crop model. A multi-environment study conducted in 2011 and 2012 included 187 recombinant inbred lines (RILs) from a bi-parental bean family to measure the effects of quantitative trait loci (QTL), environment (E), and QTLxE interactions across five sites. The dynamic mixed linear model from Vallejos et al. (2020) was modified in this study to create a dynamic module that was then integrated into the CSM-CROPGRO-Drybean model. This new hybrid crop model, with the gene-based flowering module replacing the original flowering component, requires allelic makeup of each genotype being simulated and daily E data. The hybrid model was compared to the original CSM model using the same E data and previously estimated GSPs to simulate time-to-flower. The integrated gene-based module simulated days of first flower agreed closely with observed values (root mean square error of 2.73 days and model efficiency of 0.90) across the five locations and 187 genotypes. The hybrid model with its gene-based module also described most of the G, E and GxE effects on time-to-flower and was able to predict final yield and other outputs simulated by the original CSM. These results provide the first evidence that dynamic crop simulation models can be transformed into gene-based models by replacing an existing process module with a gene-based module for simulating the same process.

genomics↗