Biology subjects

Yandeau-Nelson, M.

Publications and source records attributed to Yandeau-Nelson, M..

4 recordsLinked to original sources

bioRxiv2024

AraRoot - A Comprehensive Genome-Scale Metabolic Model for the Arabidopsis Root System.

Being the first plant to have its genome sequenced, Arabidopsis thaliana (Arabidopsis) is a well-established genetic model plant system. Studies on Arabidopsis have provided major insights into plants physiological and biochemical nature. Methods that allow us to computationally study the metabolism of organisms include the use of genome-scale metabolic models (GEMs). Despite its popularity, currently no GEM maps the metabolic activity in the roots of Arabidopsis, which is the organ that faces and responds to stress conditions in the soil. Weve developed a comprehensive GEM of the Arabidopsis root system - AraRoot. The final model includes 2,682 reactions, 2,748 metabolites, and 1,310 genes. Analyzing the metabolic pathways in the model identified 158 possible bottleneck genes that impact biomass production, most of which were found to be related to phosphorous-containing- and energy-related pathways. Further insights into tissue-specific metabolic reprogramming conclude that the cortex layer in the roots is likely responsible for root growth under prolonged exposure to high salt conditions, while the endodermis and epidermis are responsible for producing metabolites responsible for increased cell wall biosynthesis. The epidermis was found to have a very poor ability to regulate its metabolism during exposure to high salt concentrations. Overall, AraRoot is the first GEM that accurately captures the comprehensive biomass formation and stress responses of the tissues in the Arabidopsis root system.

systems biology↗

bioRxiv2021

Predicting Moisture Content During Maize Nixtamalization Using Machine Learning with NIR Spectroscopy

Lack of high throughput phenotyping systems for determining moisture content during the maize nixtamalization cooking process has led to difficulty in breeding for this trait. This study provides a high throughput, quantitative measure of kernel moisture content during nixtamalization based on NIR scanning of uncooked maize kernels. Machine learning was utilized to develop models based on the combination of NIR spectra and moisture content determined from a scaled-down benchtop cook method. A linear support vector machine (SVM) model with a Spearmans rank correlation coefficient of 0.852 between wet lab and predicted values was developed from 100 diverse temperate genotypes grown in replicate across two environments. This model was applied to NIR data from 501 diverse temperate genotypes grown in replicate in five environments. Analysis of variance revealed environment explained the highest percent of the variation (51.5%), followed by genotype (15.6%) and genotype-by-environment interaction (11.2%). A genome-wide association study identified 26 significant loci across five environments that explained between 5.04% and 16.01% (average = 10.41%). However, genome-wide markers explained 10.54% to 45.99% (average = 31.68%) of the variation, indicating the genetic architecture of this trait is likely complex and controlled by many loci of small effect. This study provides a high-throughput method to evaluate moisture content during nixtamalization that is feasible at the scale of a breeding program and provides important information about the factors contributing to variation of this trait for breeders and food companies to make future strategies to improve this important processing trait. Key MessageMoisture content during nixtamalization can be accurately predicted from NIR spectroscopy when coupled with a support vector machine (SVM) model, is strongly modulated by the environment, and has a complex genetic architecture.

plant biology↗

bioRxiv2021

Genetic Architecture of Kernel Compositional Variation in a Maize Diversity Panel

Maize (Zea mays L.) is a multi-purpose row crop grown worldwide, which overtime has often been bred for increased yield at the detriment of lower composition grain quality. Some knowledge of the genetic factors that affect quality traits has been discovered through the study of classical maize mutants. However, much of the underlying genetic architecture controlling these traits and the interaction between these traits remains unknown. To better understand variation that exists for grain compositional traits in maize, we evaluated 501 diverse temperate maize inbred lines in five unique environments and predicted 16 compositional traits (e.g. carbohydrates, protein, starch) based on the output of near-infrared (NIR) spectroscopy. Phenotypic analysis found substantial variation for compositional traits and the majority of variation was explained by genetic and environmental factors. Correlations and trade-offs among traits in different maize types (e.g. dent, sweetcorn, popcorn) were explored and significant differences and correlations were detected. In total, 22.9-71.1% of the phenotypic variation across these traits could be explained using 2,386,666 single nucleotide polymorphism (SNP) markers generated from whole genome resequencing data. A genome-wide association study (GWAS) was conducted using these same markers and found 70 statistically significant loci for 12 compositional traits. This study provides valuable insights in the phenotypic variation and genetic architecture underlying compositional traits that can be used in breeding programs for improving maize grain quality. Core IdeasO_LIUnderstanding kernel compositional variation is important for food grade corn improvement. C_LIO_LIGenetic and environmental factors account for most of the variation in compositional traits. C_LIO_LIA broad range in trait heritabilities was observed across compositional traits. C_LIO_LICompositional trade-offs will be important to consider when conducting multitrait breeding. C_LIO_LICompositional traits are mostly controlled by a large number of small effect loci. C_LI

plant biology↗

bioRxiv2020

Evaluation of the functional role of the maize Glossy2 and Glossy2-like genes in cuticular lipid deposition

Plant epidermal cells express unique molecular machinery that juxtapose the assembly of intracellular lipid components and the unique extracellular cuticular lipids that are unidirectionally secreted to plant surfaces. In maize (Zea mays L.), mutations at the glossy2 (gl2) locus affect the deposition of extracellular cuticular lipids. Sequence-based genome scanning identified a novel gl2 homolog in the maize genome, Gl2-like. Sequence homology identifies that both the Gl2-like and Gl2 genes are members of the BAHD superfamily of acyltransferases, with close sequence homology to the Arabidopsis CER2 gene. Transgenic experiments demonstrate that Gl2-like and Gl2 functionally complement the Arabidopsis cer2 mutation, with differential impacts on the cuticular lipids and the lipidome of the plant, particularly affecting the longer alkyl chain acyl lipids, particularly at the 32-carbon chain length. Site-directed mutagenesis of the putative BAHD catalytic HXXXDX-motif indicates that Gl2-like requires this catalytic capability to fully complement the cer2 function, but Gl2 can accomplish this without the need for this catalytic motif. These findings demonstrate that both Gl2 and Gl2-like overlap in their cuticular lipid function, however the two genes have evolutionary diverged to acquire non-overlapping functions. One-sentence summaryTransgenesis dissection of the functional roles of the maize Glossy2 and Glossy2-Like genes in cuticular lipid deposition.

biochemistry↗