Dataset Information

Growth and Biosynthesis of Phenolic Compounds of Canola (Brassica napus L.) to Different Ultraviolet (UV)-B Wavelengths in a Plant Factory with Artificial Light.

ABSTRACT: The application of ultraviolet-B (UV-B) irradiation to supplement visible light as an elicitor to increase bioactive compounds under controlled conditions is increasing. This study aimed to evaluate the effects of UV-B dose and wavelength region (280-300 and 300-320 nm) on the morphological, physiological, and biochemical responses of canola plants (Brassica napus L.). Canola plants (17 days after sowing) were subjected to various UV-B intensities (i.e., 0.3, 0.6, and 0.9 W m^-2) and were divided into cut and non-cut treatments for each UV treatment. Plant growth parameters exhibited different trends based on the treated UV irradiation intensity. Plant growth gradually decreased as the UV irradiation intensity and exposure time increased. Despite the same UV irradiation intensity, plant response varied significantly depending on the presence or absence of a short-wavelength cut filter (<300 nm). Canola plants suffered more leaf damage in nonfilter treatments containing shorter wavelengths (280-300 nm). UV treatment effectively activates the expression of secondary metabolite biosynthetic genes, differing depending on the UV irradiation intensity. Our results suggest that both UV irradiation intensity and wavelength should be considered when enhancing antioxidant phytochemicals without inhibiting plant growth in a plant factory with artificial light.

SUBMITTER: Lee JH

PROVIDER: S-EPMC9268760 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Genomic selection (GS) is a modern breeding approach where genome-wide single-nucleotide polymorphism (SNP) marker profiles are simultaneously used to estimate performance of untested genotypes. In this study, the potential of genomic selection methods to predict testcross performance for hybrid canola breeding was applied for various agronomic traits based on genome-wide marker profiles. A total of 475 genetically diverse spring-type canola pollinator lines were genotyped at 24,403 single-copy, genome-wide SNP loci. In parallel, the 950 F1 testcross combinations between the pollinators and two representative testers were evaluated for a number of important agronomic traits including seedling emergence, days to flowering, lodging, oil yield and seed yield along with essential seed quality characters including seed oil content and seed glucosinolate content. A ridge-regression best linear unbiased prediction (RR-BLUP) model was applied in combination with 500 cross-validations for each trait to predict testcross performance, both across the whole population as well as within individual subpopulations or clusters, based solely on SNP profiles. Subpopulations were determined using multidimensional scaling and K-means clustering. Genomic prediction accuracy across the whole population was highest for seed oil content (0.81) followed by oil yield (0.75) and lowest for seedling emergence (0.29). For seed yieId, seed glucosinolate, lodging resistance and days to onset of flowering (DTF), prediction accuracies were 0.45, 0.61, 0.39 and 0.56, respectively. Prediction accuracies could be increased for some traits by treating subpopulations separately; a strategy which only led to moderate improvements for some traits with low heritability, like seedling emergence. No useful or consistent increase in accuracy was obtained by inclusion of a population substructure covariate in the model. Testcross performance prediction using genome-wide SNP markers shows considerable potential for pre-selection of promising hybrid combinations prior to resource-intensive field testing over multiple locations and years.

Dataset Information

Growth and Biosynthesis of Phenolic Compounds of Canola (Brassica napus L.) to Different Ultraviolet (UV)-B Wavelengths in a Plant Factory with Artificial Light.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets