Measuring polyphenol oxidase activity in a wheat breeding program

1999 ◽  
Vol 79 (4) ◽  
pp. 507-514 ◽  
Author(s):  
T. N. Mccaig ◽  
D. Y. K. Fenn ◽  
R. E. Knox ◽  
R. M. Depauw ◽  
J. M. Clarke ◽  
...  
Keyword(s):  
Polyphenol Oxidase ◽  
Large Scale ◽  
Water Extract ◽  
Wheat Breeding ◽  
Screening Tools ◽  
Breeding Programs ◽  
Spring Triticale ◽  
The Mean ◽  
Whole Seed ◽  
End Use

High levels of polyphenol oxidase (PPO) have been associated with discoloration of end-use products of wheat, especially certain noodle types. Two whole-seed methods of measuring PPO, one based on 10 mM tyrosine as substrate and the other on 90 mM catechol, were examined and modified to determine their potential as screening tools in large-scale breeding programs. Thirteen spring wheat and two spring triticale genotypes were used to compare the methods. Both methods could measure PPO on individual seeds. All genotypes displayed large seed-to-seed variation for PPO with both substrates. The mean coefficient of variation for the PPO values of individual seeds within genotypes was 39% with tyrosine and 34% with catechol. Furthermore, the PPO values of individual seeds within genotypes were not normally distributed for most genotypes. Identifying genotypes with incremental improvements in PPO would probably require measurement of 70–100 seeds. Approximately 50% of the catecholase activity was associated with the water extract after soaking seeds for 16 h, while all of the tyrosinase activity was still associated with the seed, suggesting that different enzymes are responsible for oxidizing tyrosine and catechol in wheat. While the 10 mM tyrosine assay was nondestructive and allowed plants to be generated from seeds low in PPO, 90 mM catechol reduced germination to less than 20%. Reducing the catechol to 30 mM improved germination to 85%, did not substrate-limit the reaction, and reduced the health risk associated with the assay. Spectral and kinetic differences between the assays were also considered. Key words: Triticum sp., wheat, polyphenol oxidase, catechol, tyrosine

scholarly journals Genetic control of seed dormancy and pre-harvest sprouting in wheat

2006 ◽  
Vol 63 (6) ◽  
pp. 564-566 ◽  
Author(s):  
Claudinei Andreoli ◽  
Manoel Carlos Bassoi ◽  
Dionisio Brunetta
Keyword(s):  
Seed Dormancy ◽  
Bimodal Distribution ◽  
Wheat Breeding ◽  
Breeding Programs ◽  
Control Seed ◽  
Grain Color ◽  
The Mean ◽  
The Cross ◽  
End Use ◽  
Financial Losses

Pre-harvest sprouting (PHS) damage leads to occasional massive losses in all wheat producing areas, causing downgrading of grain quality, that severely limits end-use applications and results in substantial financial losses to farmers and food processors. Red grain color is a traditional marker for resistance to sprouting in wheat breeding programs, however red-grained genotype alone does not always guarantee effective resistance. The objective of this work was to find genes for resistance to PHS and investigate its inheritance in Brazilian wheat cultivars. Genetic variation for dormancy was investigated in the parents, F1 and 300 F2 lines derived from the cross Frontana × OR1 and its reciprocal. The germination/dormancy sprouted grains was evaluated on fifty seeds per replication, germinated in paper towel rolls at 20ºC for 5 days. A bimodal distribution for dormancy occurred in the Frontana/OR1 and OR1/Frontana derived F2 populations. The mean ratio of dormant and non-dormant seeds of the cross and its reciprocal was 85:1115, fitting a digenic model of 1:15 (P < 0.05). In fact, all non after-ripened F1 seeds germinated. The F2 distribution indicates that two major genes, here called A,a and B,b, control seed dormancy, which it appears to be recessive to dormancy. Only the homozygous aabb is dormant. As expected, there was no effect of maternal tissues.

scholarly journals Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models

2021 ◽  
Author(s):  
Karansher S Sandhu ◽  
Meriem Aoun ◽  
Craig Morris ◽  
Arron H Carter
Keyword(s):  
Deep Learning ◽  
Grain Yield ◽  
Cross Validation ◽  
Wheat Breeding ◽  
Breeding Program ◽  
Quality Traits ◽  
Learning Models ◽  
Breeding Programs ◽  
Wheat Breeding Program ◽  
End Use

Breeding for grain yield, biotic and abiotic stress resistance, and end-use quality are important goals of wheat breeding programs. Screening for end-use quality traits is usually secondary to grain yield due to high labor needs, cost of testing, and large seed requirements for phenotyping. Hence, testing is delayed until later stages in the breeding program. Delayed phenotyping results in advancement of inferior end-use quality lines into the program. Genomic selection provides an alternative to predict performance using genome-wide markers. Due to large datasets in breeding programs, we explored the potential of the machine and deep learning models to predict fourteen end-use quality traits in a winter wheat breeding program. The population used consisted of 666 wheat genotypes screened for five years (2015-19) at two locations (Pullman and Lind, WA, USA). Nine different models, including two machine learning (random forest and support vector machine) and two deep learning models (convolutional neural network and multilayer perceptron), were explored for cross-validation, forward, and across locations predictions. The prediction accuracies for different traits varied from 0.45-0.81, 0.29-0.55, and 0.27-0.50 under cross-validation, forward, and across location predictions. In general, forward prediction accuracies kept increasing over time due to increments in training data size and was more evident for machine and deep learning models. Deep learning models performed superior over the traditional ridge regression best linear unbiased prediction (RRBLUP) and Bayesian models under all prediction scenarios. The high accuracy observed for end-use quality traits in this study support predicting them in early generations, leading to the advancement of superior genotypes to more extensive grain yield trailing. Furthermore, the superior performance of machine and deep learning models strengthen the idea to include them in large scale breeding programs for predicting complex traits.

scholarly journals Late-maturity α-amylase (LMA): exploring the underlying mechanisms and end-use quality effects in wheat

Planta ◽  
2021 ◽  
Vol 255 (1) ◽  
Author(s):  
Ashley E. Cannon ◽  
Elliott J. Marston ◽  
Alecia M. Kiszonas ◽  
Amber L. Hauvermale ◽  
Deven R. See
Keyword(s):  
Wheat Breeding ◽  
Protein Accumulation ◽  
Comprehensive Overview ◽  
Breeding Programs ◽  
Future Directions ◽  
Open Questions ◽  
Late Maturity ◽  
End Use ◽  
Molecular Aspects ◽  
Underlying Mechanisms

Abstract Main conclusion A comprehensive understanding of LMA from the underlying molecular aspects to the end-use quality effects will greatly benefit the global wheat industry and those whose livelihoods depend upon it. Abstract Late-maturity α-amylase (LMA) leads to the expression and protein accumulation of high pI α-amylases during late grain development. This α-amylase is maintained through harvest and leads to an unacceptable low falling number (FN), the wheat industry’s standard measure for predicting end-use quality. Unfortunately, low FN leads to significant financial losses for growers. As a result, wheat researchers are working to understand and eliminate LMA from wheat breeding programs, with research aims that include unraveling the genetic, biochemical, and physiological mechanisms that lead to LMA expression. In addition, cereal chemists and quality scientists are working to determine if and how LMA-affected grain impacts end-use quality. This review is a comprehensive overview of studies focused on LMA and includes open questions and future directions.

scholarly journals High molecular weight glutenin gene diversity in Aegilops tauschii demonstrates unique origin of superior wheat quality

2021 ◽  
Author(s):  
Emily Delorean ◽  
LiangLiang Gao ◽  
Jose Fausto Cervantes Lopez ◽  
The Open Wild Wheat Consortium ◽  
Brande Wulff ◽  
...  
Keyword(s):  
Gene Diversity ◽  
Aegilops Tauschii ◽  
Tetraploid Wheat ◽  
Wheat Breeding ◽  
D Genome ◽  
Breeding Programs ◽  
Lineage 2 ◽  
End Use ◽  
Genomic Resource ◽  
New Alleles

Abstract Central to the diversity of wheat products was the origin of hexaploid bread wheat, which added the D-genome of Aegilops tauschii to tetraploid wheat giving rise to superior dough properties in leavened breads. The polyploidization, however, imposed a genetic bottleneck, with only limited diversity introduced in the wheat D-subgenome. To understand genetic variants for quality, we sequenced 273 accessions spanning the known diversity of Ae. tauschii. We discovered 45 haplotypes in Glu-D1, a major determinant of quality, relative to the two predominant haplotypes in wheat. The wheat allele 2+12 was found in Ae. tauschii Lineage 2, the donor of the wheat D-subgenome. Conversely, the superior quality wheat allele 5+10 allele originated in Lineage 3, a recently characterized lineage of Ae. tauschii, showing a unique origin of this important allele. These two wheat alleles were also quite similar relative to the total observed molecular diversity in Ae. tauschii at Glu-D1. Ae. tauschii is thus a reservoir for unique Glu-D1 alleles and provides the genomic resource to begin utilizing new alleles for end-use quality improvement in wheat breeding programs.

scholarly journals A Rapid, Reliable RP-UPLC Method for Large-Scale Analysis of Wheat HMW-GS Alleles

Molecules ◽  
2021 ◽  
Vol 26 (20) ◽  
pp. 6174
Author(s):  
Su-Bin Lee ◽  
Yu-Jeong Yang ◽  
Sun-Hyung Lim ◽  
Yong Q. Gu ◽  
Jong-Yeol Lee
Keyword(s):  
Large Scale ◽  
Storage Proteins ◽  
Ultra Performance Liquid Chromatography ◽  
Reversed Phase ◽  
Wheat Breeding ◽  
Pcr Analysis ◽  
Large Scale Analysis ◽  
End Use ◽  
Acquity Uplc ◽  
Wheat Storage Proteins

High-molecular-weight glutenin subunits (HMW-GS) account for only 10% of total wheat storage proteins, but play an important role in the processing quality of wheat flour. Therefore, identifying HMW-GS alleles associated with good end-use quality provides important information for wheat breeders. To rapidly, accurately and reproducibly identify HMW-GS, we established an optimized reversed-phase ultra-performance liquid chromatography (RP-UPLC) method. Separation parameters were optimized using an ACQUITY UPLC Protein BEH C4 column and stepwise ACN gradient, and the separation patterns and retention times (RTs) of 22 subunits were comparatively analyzed in 16 standard wheat cultivars. All HMW-GS proteins were well separated within about 5.5 min, and all analyses were complete within 12 min. We distinguished the 16 subunits based on RT, although three subunits in 1Bx (1Bx7/1Bx7OE and 1Bx17) and three subunits in 1By (1By8*, 1By9 and 1By15) had overlapping RTs; these were differentiated by SDS-PAGE. To distinguish 1Bx7 and 1Bx7OE, which differ in protein abundance, RP-UPLC was combined with PCR analysis of DNA junction markers. The optimized method was successfully applied to determine HMW-GS alleles in a large collection of bread wheat germplasm (1787 lines). This protocol is an appropriate option for selecting lines harboring favorable HMW-GS alleles in wheat breeding.

scholarly journals High molecular weight glutenin gene diversity in Aegilops tauschii demonstrates unique origin of superior wheat quality

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Emily Delorean ◽  
Liangliang Gao ◽  
Jose Fausto Cervantes Lopez ◽  
Ali Mehrabi ◽  
Alison Bentley ◽  
...  
Keyword(s):  
Gene Diversity ◽  
Aegilops Tauschii ◽  
Tetraploid Wheat ◽  
Wheat Breeding ◽  
D Genome ◽  
Breeding Programs ◽  
Lineage 2 ◽  
End Use ◽  
Genomic Resource ◽  
New Alleles

AbstractCentral to the diversity of wheat products was the origin of hexaploid bread wheat, which added the D-genome of Aegilops tauschii to tetraploid wheat giving rise to superior dough properties in leavened breads. The polyploidization, however, imposed a genetic bottleneck, with only limited diversity introduced in the wheat D-subgenome. To understand genetic variants for quality, we sequenced 273 accessions spanning the known diversity of Ae. tauschii. We discovered 45 haplotypes in Glu-D1, a major determinant of quality, relative to the two predominant haplotypes in wheat. The wheat allele 2 + 12 was found in Ae. tauschii Lineage 2, the donor of the wheat D-subgenome. Conversely, the superior quality wheat allele 5 + 10 allele originated in Lineage 3, a recently characterized lineage of Ae. tauschii, showing a unique origin of this important allele. These two wheat alleles were also quite similar relative to the total observed molecular diversity in Ae. tauschii at Glu-D1. Ae. tauschii is thus a reservoir for unique Glu-D1 alleles and provides the genomic resource to begin utilizing new alleles for end-use quality improvement in wheat breeding programs.

scholarly journals Deciphering the Genetics of Major End-Use Quality Traits in Wheat

2019 ◽  
Author(s):  
Sepehr Mohajeri Naraghi ◽  
Senay Simsek ◽  
Ajay Kumar ◽  
S.M. Hisam Al Rabbi ◽  
Mohammed S. Alamri ◽  
...  
Keyword(s):  
Recombinant Inbred Lines ◽  
Wheat Breeding ◽  
Snp Markers ◽  
Peak Time ◽  
Quality Traits ◽  
Gene Encoding ◽  
Breeding Programs ◽  
B1 Gene ◽  
A Subunit ◽  
End Use

AbstractImproving the end-use quality traits is one of the primary objectives in wheat breeding programs. In the current study, a population of 127 recombinant inbred lines (RILs) derived from a cross between Glenn (PI-639273) and Traverse (PI-642780) was developed and used to identify quantitative trait loci (QTL) for 16 end-use quality traits in wheat. The phenotyping of these 16 traits was performed in nine environments in North Dakota, USA. The genotyping for the RIL population was conducted using the wheat Illumina iSelect 90K SNP assay. A high-density genetic linkage map consisting of 7,963 SNP markers identified a total of 76 additive QTL (A-QTL) and 73 digenic epistatic QTL (DE-QTL) associated with these traits. Overall, 12 stable major A-QTL and three stable DE-QTL were identified for these traits, suggesting that both A-QTL and DE-QTL played an important role in controlling end-use quality traits in wheat. The most significant A-QTL (AQ.MMLPT.ndsu.1B) was detected on chromosome 1B for mixograph middle line peak time. The AQ.MMLPT.ndsu.1B A-QTL was located very close to the position of the Glu-B1 gene encoding for a subunit of high molecular weight glutenin and explained up to 24.43% of phenotypic variation for mixograph MID line peak time. A total of 23 co-localized QTL loci were detected, suggesting the possibility of the simultaneous improvement of the end-use quality traits through selection procedures in wheat breeding programs. Overall, the information provided in this study could be used in marker-assisted selection to increase selection efficiency and to improve the end-use quality in wheat.

scholarly journals Incorporating Molecular Markers and Causal Structure among Traits Using a Smith-Hazel Index and Structural Equation Models

Agronomy ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1953
Author(s):  
Juan Valente Hidalgo-Contreras ◽  
Josafhat Salinas-Ruiz ◽  
Kent M. Eskridge ◽  
Stephen P. Baenziger
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Causal Structure ◽  
Wheat Breeding ◽  
Causal Relationships ◽  
Breeding Programs ◽  
Path Coefficients ◽  
Selection Indices ◽  
Superior Genotypes ◽  
The Mean

The goal in breeding programs is to choose candidates that produce offspring with the best phenotypes. In conventional selection, the best candidate is selected with high genotypic values (unobserved), in the assumption that this is related to the observed phenotypic values for several traits. Multi-trait selection indices are used to identify superior genotypes when a number of traits are to be considered simultaneously. Often, the causal relationship among the traits is well known. Structural equation models (SEM) have been used to describe the causal relationships among variables in many biological systems. We present a method for multi-trait genomic selection that incorporates causal relationships among traits by coupling SEM with a Smith–Hazel index that incorporates markers. The method was applied to field data from a Nebraska winter wheat breeding program. We found that the correlation and the relative efficiency increased for the proposed Smith–Hazel indices when the total causal information among traits was accounted for by the vector of weights (b), which includes the causal path coefficients in the causal matrix (Λ). On the other hand, when selection was based on a primary trait, for example yield, the proposed SI increased the mean yield of the best 28 (Top 10%) genotypes to 7%.

Puroindoline genes and their effects on grain quality traits in southern Australian wheat cultivars

2004 ◽  
Vol 55 (1) ◽  
pp. 89 ◽  
Author(s):  
Karen Cane ◽  
Merrin Spackman ◽  
H. A. Eagles
Keyword(s):  
Water Absorption ◽  
Grain Quality ◽  
High Water ◽  
Major Effect ◽  
Wheat Breeding ◽  
Quality Data ◽  
Breeding Programs ◽  
Australian Wheat ◽  
End Use ◽  
Milling Yield

Grain hardness is a major determinant of the classification and end-use of wheat. Two genes, Pina-D1 and Pinb-D1, have a major effect on this trait, so for wheat breeding programs it is important to identify the alleles of these genes present in elite germplasm. This study was conducted to identify the alleles present in southern Australian germplasm, and to determine if they affected quality characteristics other than grain hardness.Only 3 genotypes were identified. These were Pina-D1a/Pinb-D1a producing soft grain, Pina-D1a/Pinb-D1b producing moderately hard grain, and Pina-D1b/Pinb-D1a producing very hard grain. WW15 was the probable source of Pina-D1a/Pinb-D1b in most cultivars; however, Halberd represented another source. An important source of Pina-D1b/Pinb-D1a was the CIMMYT line Pavon, with sources from the old Australian cultivars Gabo and Falcon probably still present in modern germplasm.In an analysis of grain quality data from the Victorian Institute for Dryland Agriculture breeding program, the Pina-D1b/Pinb-D1a genotype had a significantly higher water absorption and significantly lower milling yield than the Pina-D1a/Pinb-D1b genotype, which indicates that these genes will impede the development of hard-grained cultivars that combine high water absorption and high milling yield.

scholarly journals Quality Control in 3D Printing: Accuracy Analysis of 3D-Printed Models of Patient-Specific Anatomy

Materials ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1021
Author(s):  
Bernhard Dorweiler ◽  
Pia Elisabeth Baqué ◽  
Rayan Chaban ◽  
Ahmed Ghazy ◽  
Oroa Salem
Keyword(s):  
Wall Thickness ◽  
Large Scale ◽  
Fused Deposition Modeling ◽  
Vascular Anatomy ◽  
3D Models ◽  
Patient Specific ◽  
Stl File ◽  
Fused Deposition ◽  
3D Printed ◽  
The Mean

As comparative data on the precision of 3D-printed anatomical models are sparse, the aim of this study was to evaluate the accuracy of 3D-printed models of vascular anatomy generated by two commonly used printing technologies. Thirty-five 3D models of large (aortic, wall thickness of 2 mm, n = 30) and small (coronary, wall thickness of 1.25 mm, n = 5) vessels printed with fused deposition modeling (FDM) (rigid, n = 20) and PolyJet (flexible, n = 15) technology were subjected to high-resolution CT scans. From the resulting DICOM (Digital Imaging and Communications in Medicine) dataset, an STL file was generated and wall thickness as well as surface congruency were compared with the original STL file using dedicated 3D engineering software. The mean wall thickness for the large-scale aortic models was 2.11 µm (+5%), and 1.26 µm (+0.8%) for the coronary models, resulting in an overall mean wall thickness of +5% for all 35 3D models when compared to the original STL file. The mean surface deviation was found to be +120 µm for all models, with +100 µm for the aortic and +180 µm for the coronary 3D models, respectively. Both printing technologies were found to conform with the currently set standards of accuracy (<1 mm), demonstrating that accurate 3D models of large and small vessel anatomy can be generated by both FDM and PolyJet printing technology using rigid and flexible polymers.

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