Dataset Information

Diversity, distribution of Puroindoline genes and their effect on kernel hardness in a diverse panel of Chinese wheat germplasm.

ABSTRACT: Kernel hardness, which has great influence on the end-use properties of common wheat, is mainly controlled by Puroindoline genes, Pina and Pinb. Using EcoTILLING platform, we herein investigated the allelic variations of Pina and Pinb genes and their association with the Single Kernel Characterization System (SKCS) hardness index in a diverse panel of wheat germplasm.The kernel hardness varied from 1.4 to 102.7, displaying a wide range of hardness index. In total, six Pina and nine Pinb alleles resulting in 15 genotypes were detected in 1787 accessions. The most common alleles are the wild type Pina-D1a (90.4%) and Pina-D1b (7.4%) for Pina, and Pinb-D1b (43.6%), Pinb-D1a (41.1%) and Pinb-D1p (12.8%) for Pinb. All the genotypes have hard type kernel hardness of SKCS index (>60.0), except the wild types of Pina and Pinb combination (Pina-D1a/Pinb-D1a). The most frequent genotypes in Chinese and foreign cultivars was Pina-D1a/Pinb-D1b (46.3 and 39.0%, respectively) and in Chinese landraces was Pina-D1a/Pinb-D1a (54.2%). The frequencies of hard type accessions are increasing from 35.5% in the region IV, to 40.6 and 61.4% in the regions III and II, and then to 77.0% in the region I, while those of soft type are accordingly decreasing along with the increase of latitude. Varieties released after 2000 in Beijing, Hebei, Shandong and Henan have higher average kernel hardness index than that released before 2000.The kernel hardness in a diverse panel of Chinese wheat germplasm revealed an increasing of kernel hardness generally along with the latitude across China. The wild type Pina-D1a and Pinb-D1a, and one Pinb mutant (Pinb-D1b) are the most common alleles of six Pina and nine Pinb alleles, and a new double null genotype (Pina-D1x/Pinb-D1ah) possessed relatively high SKCS hardness index. More hard type varieties were released in recent years with different prevalence of Pin-D1 combinations in different regions. This work would benefit the understanding of the selection and molecular processes of kernel hardness across China and different breeding stages, and provide useful information for the improvement of wheat quality in China.

SUBMITTER: Ma X

PROVIDER: S-EPMC5607584 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Diversity, distribution of Puroindoline genes and their effect on kernel hardness in a diverse panel of Chinese wheat germplasm.

Ma Xiaoling X Sajjad Muhammad M Wang Jing J Yang Wenlong W Sun Jiazhu J Li Xin X Zhang Aimin A Liu Dongcheng D

BMC plant biology 20170920 1

<h4>Background</h4>Kernel hardness, which has great influence on the end-use properties of common wheat, is mainly controlled by Puroindoline genes, Pina and Pinb. Using EcoTILLING platform, we herein investigated the allelic variations of Pina and Pinb genes and their association with the Single Kernel Characterization System (SKCS) hardness index in a diverse panel of wheat germplasm.<h4>Results</h4>The kernel hardness varied from 1.4 to 102.7, displaying a wide range of hardness index. In tot ...[more]

PMID: 28931378

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Project description:Grain hardness is an important quality trait of cereal crops. In wheat, it is mainly determined by the Hardness locus that harbors genes encoding puroindoline A (PINA) and puroindoline B (PINB). Any deletion or mutation of these genes leading to the absence of PINA or to single amino acid changes in PINB leads to hard endosperms. Although it is generally acknowledged that hardness is controlled by adhesion strength between the protein matrix and starch granules, the physicochemical mechanisms connecting puroindolines and the starch-protein interactions are unknown as of this time. To explore these mechanisms, we focused on PINA. The overexpression in a hard wheat cultivar (cv. Courtot with the Pina-D1a and Pinb-D1d alleles) decreased grain hardness in a dose-related effect, suggesting an interactive process. When PINA was added to gliadins in solution, large aggregates of up to 13 μm in diameter were formed. Turbidimetry measurements showed that the PINA-gliadin interaction displayed a high cooperativity that increased with a decrease in pH from neutral to acid (pH 4) media, mimicking the pH change during endosperm development. No turbidity was observed in the presence of isolated α- and γ-gliadins, but non-cooperative interactions of PINA with these proteins could be confirmed by surface plasmon resonance. A significant higher interaction of PINA with γ-gliadins than with α-gliadins was observed. Similar binding behavior was observed with a recombinant repeated polypeptide that mimics the repeat domain of gliadins, i.e., (Pro-Gln-Gln-Pro-Tyr)8. Taken together, these results suggest that the interaction of PINA with a monomeric gliadin creates a nucleation point leading to the aggregation of other gliadins, a phenomenon that could prevent further interaction of the storage prolamins with starch granules. Consequently, the role of puroindoline-prolamin interactions on grain hardness should be addressed on the basis of previous observations that highlight the similar subcellular routing of storage prolamins and puroindolines.

Project description:BACKGROUND: Grain texture is one of the most important characteristics in bread wheat (Triticum aestivum L.). Puroindoline-D1 genes play the main role in controlling grain texture and are intimately associated with the milling and processing qualities in bread wheat. RESULTS: A series of diagnostic molecular markers and dCAPS markers were used to characterize Pina-D1 and Pinb-D1 in 493 wheat cultivars from diverse geographic locations. A primer walking strategy was used to characterize PINA-null alleles at the DNA level. Results indicated that Chinese landraces encompassing 12 different Puroindoline-D1 allelic combinations showed the highest diversity, while CIMMYT wheat cultivars containing 3 different Puroindoline-D1 allelic combinations showed the lowest diversity amongst wheat cultivars from the five countries surveyed. Two novel Pina-D1 alleles, designated Pina-D1s with a 4,422-bp deletion and Pina-D1u with a 6,460-bp deletion in the Ha (Hardness) locus, were characterized at the DNA level by a primer walking strategy, and corresponding molecular markers Pina-N3 and Pina-N4 were developed for straightforward identification of the Pina-D1s and Pina-D1u alleles. Analysis of the association of Puroindoline-D1 alleles with grain texture indicated that wheat cultivars with Pina-null/Pinb-null allele, possessing an approximate 33-kb deletion in the Ha locus, have the highest SKCS hardness index amongst the different genotypes used in this study. Moreover, wheat cultivars with the PINA-null allele have significantly higher SKCS hardness index than those of Pinb-D1b and Pinb-D1p alleles. CONCLUSIONS: Molecular characterization of the Puroindoline-D1 allele was investigated in bread wheat cultivars from five geographic regions, resulting in the discovery of two new alleles - Pina-D1s and Pina-D1u. Molecular markers were developed for both alleles. Analysis of the association of the Puroindoline-D1 alleles with grain texture showed that cultivars with PINA-null allele possessed relatively high SKCS hardness index. This study can provide useful information for the improvement of wheat quality, as well as give a deeper understanding of the molecular and genetic processes controlling grain texture in bread wheat.

Project description:Wheat blast is an emerging threat to wheat production, due to its recent migration to South Asia and Sub-Saharan Africa. Because genomic selection (GS) has emerged as a promising breeding strategy, the key objective of this study was to evaluate it for wheat blast phenotyped at precision phenotyping platforms in Quirusillas (Bolivia), Okinawa (Bolivia) and Jashore (Bangladesh) using three panels: (i) a diversity panel comprising 172 diverse spring wheat genotypes, (ii) a breeding panel comprising 248 elite breeding lines, and (iii) a full-sibs panel comprising 298 full-sibs. We evaluated two genomic prediction models (the genomic best linear unbiased prediction or GBLUP model and the Bayes B model) and compared the genomic prediction accuracies with accuracies from a fixed effects model (with selected blast-associated markers as fixed effects), a GBLUP + fixed effects model and a pedigree relationships-based model (ABLUP). On average, across all the panels and environments analyzed, the GBLUP + fixed effects model (0.63 ± 0.13) and the fixed effects model (0.62 ± 0.13) gave the highest prediction accuracies, followed by the Bayes B (0.59 ± 0.11), GBLUP (0.55 ± 0.1), and ABLUP (0.48 ± 0.06) models. The high prediction accuracies from the fixed effects model resulted from the markers tagging the 2NS translocation that had a large effect on blast in all the panels. This implies that in environments where the 2NS translocation-based blast resistance is effective, genotyping one to few markers tagging the translocation is sufficient to predict the blast response and genome-wide markers may not be needed. We also observed that marker-assisted selection (MAS) based on a few blast-associated markers outperformed GS as it selected the highest mean percentage (88.5%) of lines also selected by phenotypic selection and discarded the highest mean percentage of lines (91.8%) also discarded by phenotypic selection, across all panels. In conclusion, while this study demonstrates that MAS might be a powerful strategy to select for the 2NS translocation-based blast resistance, we emphasize that further efforts to use genomic tools to identify non-2NS translocation-based blast resistance are critical.

Dataset Information

Diversity, distribution of Puroindoline genes and their effect on kernel hardness in a diverse panel of Chinese wheat germplasm.

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Diversity, distribution of Puroindoline genes and their effect on kernel hardness in a diverse panel of Chinese wheat germplasm.

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