Dataset Information

Relationship between Mycotoxin Content in Winter Wheat Grain and Aspirated Dust Collected during Harvest and after Storage.

ABSTRACT: A total of 323 paired grain and grain dust samples (particle size <1650 ?m) were collected from combines at harvest (56%), on-farm bins (28%), and experimental minibins seeded with an ochratoxin A (OTA)/Penicillium verrucosum hot spots (15%) of which >98% were soft red winter wheat. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to detect 21 mycotoxins, including deoxynivalenol (DON) and its plant-conjugated form, deoxynivalenol 3-?-d-glucoside (DON 3-Glc). Except for DON 3-Glc, all mycotoxin concentrations found in grain dust were higher than in grain (p < 0.0030). Pearson correlation coefficients and two-variable regression show a significant (p < 0.0001) linear relationship between the mycotoxin content in grain and that in grain dust with 19 toxins. In only five mycotoxins (DON, OTA, ochratoxin B, citrinin, and enniatin A₁), more than 82% of the variation in the data is explained by the two-variable regression model. Because of its higher mean concentration and detection frequency, only DON produced a strong relationship (p < 0.0001, r ² = 0.949) with low root-mean-square error (RMSE) (293.41 ng/g). The results suggest that modeling levels in grain based upon levels in grain dust can be used to estimate DON in grain bulk.

SUBMITTER: Limay-Rios V

PROVIDER: S-EPMC7841775 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Relationship between Mycotoxin Content in Winter Wheat Grain and Aspirated Dust Collected during Harvest and after Storage.

Limay-Rios Victor V Schaafsma Arthur W AW

ACS omega 20210111 3

A total of 323 paired grain and grain dust samples (particle size <1650 μm) were collected from combines at harvest (56%), on-farm bins (28%), and experimental minibins seeded with an ochratoxin A (OTA)/<i>Penicillium verrucosum</i> hot spots (15%) of which >98% were soft red winter wheat. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to detect 21 mycotoxins, including deoxynivalenol (DON) and its plant-conjugated form, deoxynivalenol 3-β-d-glucoside (DON 3-Glc). Except for ...[more]

PMID: 33521426

Similar Datasets

Project description:Remote sensing has been used as an important means of modern crop production monitoring, especially for wheat quality prediction in the middle and late growth period. In order to further improve the accuracy of estimating grain protein content (GPC) through remote sensing, this study analyzed the quantitative relationship between 14 remote sensing variables obtained from images of environment and disaster monitoring and forecasting small satellite constellation system equipped with wide-band CCD sensors (abbreviated as HJ-CCD) and field-grown winter wheat GPC. The 14 remote sensing variables were normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), optimized soil-adjusted vegetation index (OSAVI), nitrogen reflectance index (NRI), green normalized difference vegetation index (GNDVI), structure intensive pigment index (SIPI), plant senescence reflectance index (PSRI), enhanced vegetation index (EVI), difference vegetation index (DVI), ratio vegetation index (RVI), Rblue (reflectance at blue band), Rgreen (reflectance at green band), Rred (reflectance at red band) and Rnir (reflectance at near infrared band). The partial least square (PLS) algorithm was used to construct and validate the multivariate remote sensing model of predicting wheat GPC. The research showed a close relationship between wheat GPC and 12 remote sensing variables other than Rblue and Rgreen of the spectral reflectance bands. Among them, except PSRI and Rblue, Rgreen and Rred, other remote sensing vegetation indexes had significant multiple correlations. The optimal principal components of PLS model used to predict wheat GPC were: NDVI, SIPI, PSRI and EVI. All these were sensitive variables to predict wheat GPC. Through modeling set and verification set evaluation, GPC prediction models' coefficients of determination (R2) were 0.84 and 0.8, respectively. The root mean square errors (RMSE) were 0.43% and 0.54%, respectively. It indicated that the PLS algorithm model predicted wheat GPC better than models for linear regression (LR) and principal components analysis (PCA) algorithms. The PLS algorithm model's prediction accuracies were above 90%. The improvement was by more than 20% than the model for LR algorithm and more than 15% higher than the model for PCA algorithm. The results could provide an effective way to improve the accuracy of remotely predicting winter wheat GPC through satellite images, and was conducive to large-area application and promotion.

Project description:The mycotoxin deoxynivalenol (DON) acts as a disease virulence factor for Fusarium fungi, and tolerance of DON enhances wheat resistance to Fusarium head blight (FHB) disease. Two variants of an ATP-binding cassette (ABC) family C transporter gene were cloned from DON-treated wheat mRNA, namely TaABCC3.1 and TaABCC3.2. These represent two of three putative genes identified on chromosomes 3A, 3B, and 3D of the wheat genome sequence. Variant TaABCC3.1 represents the DON-responsive transcript previously associated with DON resistance in wheat. PCR-based mapping and in silico sequence analyses located TaABCC3.1 to the short arm of wheat chromosome 3B (not within the FHB resistance quantitative trait locus Fhb1). In silico analyses of microarray data indicated that TaABCC3 genes are expressed in reproductive tissue and roots, and in response to the DON producer Fusarium graminearum. Gene expression studies showed that TaABCC3.1 is activated as part of the early host response to DON and in response to the FHB defence hormone jasmonic acid. Virus-induced gene silencing (VIGS) confirmed that TaABCC3 genes contributed to DON tolerance. VIGS was performed using two independent viral construct applications: one specifically targeted TaABCC3.1 for silencing, while the other targeted this gene and the chromosome 3A homeologue. In both instances, VIGS resulted in more toxin-induced discoloration of spikelets, compared with the DON effects in non-silenced spikelets at 14 d after toxin treatment (≥2.2-fold increase, P<0.05). Silencing by both VIGS constructs enhanced head ripening, and especially so in DON-treated heads. VIGS of TaABCC3 genes also reduced the grain number by more than 28% (P<0.05), both with and without DON treatment, and the effects were greater for the construct that targeted the two homeologues. Hence, DON-responsive TaABCC3 genes warrant further study to determine their potential as disease resistance breeding targets and their function in grain formation and ripening.

Project description:Increased genetic gain for complex traits in plant breeding programs can be achieved through different selection strategies. The objective of this study was to compare potential gains for grain yield in a winter wheat breeding program through estimating response to selection R values across several selection approaches including phenotypic (PS), marker-based (MS), genomic (GS), and a combination of PS and GS (PS+GS). Ten populations of Washington State University (WSU) winter wheat breeding lines including a diversity panel and F5 and double haploid lines evaluated from 2015 to 2019 growing seasons for grain yield in Lind and Pullman, WA, USA were used in the study. Selection was conducted by selecting the top 20% of lines based on observed yield (PS strategy), genomic estimated breeding values (GS), presence of yield "enhancing" alleles of the most significant single nucleotide polymorphism (SNP) markers identified from genome-wide association mapping (MS), and high observed yield and estimated breeding values (PS+GS). Overall, PS compared to other individual selection strategies (MS and GS) showed the highest mean response (R = 0.61) within the same environment. When combined with GS, a 23% improvement in R for yield was observed, indicating that gains could be improved by complementing traditional PS with GS within the same environment. Validating selection strategies in different environments resulted in low to negative R values indicating the effects of genotype-by-environment interactions for grain yield. MS was not successful in terms of R relative to the other selection approaches; using this strategy resulted in a significant (P < 0.05) decrease in response to selection compared with the other approaches. An integrated PS+GS approach could result in optimal genetic gain within the same environment, whereas a PS strategy might be a viable option for grain yield validated in different environments. Altogether, we demonstrated that gains through increased response to selection for yield could be achieved in the WSU winter wheat breeding program by implementing different selection strategies either exclusively or in combination.

Project description:BackgroundThe future productivity of wheat (T. aestivum L.) as the most grown crop worldwide is of utmost importance for global food security. Thousand kernel weight (TKW) in wheat is closely associated with grain architecture-related traits, e.g. kernel length (KL), kernel width (KW), kernel area (KA), kernel diameter ratio (KDR), and factor form density (FFD). Discovering the genetic architecture of natural variation in these traits, identifying QTL and candidate genes are the main aims of this study. Therefore, grain architecture-related traits in 261 worldwide winter accessions over three field-year experiments were evaluated.ResultsGenome-wide association analysis using 90K SNP array in FarmCPU model revealed several interesting genomic regions including 17 significant SNPs passing false discovery rate threshold and strongly associated with the studied traits. Four of associated SNPs were physically located inside candidate genes within LD interval e.g. BobWhite_c5872_589 (602,710,399 bp) found to be inside TraesCS6A01G383800 (602,699,767-602,711,726 bp). Further analysis reveals the four novel candidate genes potentially involved in more than one grain architecture-related traits with a pleiotropic effects e.g. TraesCS6A01G383800 gene on 6A encoding oxidoreductase activity was associated with TKW and KA. The allelic variation at the associated SNPs showed significant differences betweeen the accessions carying the wild and mutated alleles e.g. accessions carying C allele of BobWhite_c5872_589, TraesCS6A01G383800 had significantly higher TKW than the accessions carying T allele. Interestingly, these genes were highly expressed in the grain-tissues, demonstrating their pivotal role in controlling the grain architecture.ConclusionsThese results are valuable for identifying regions associated with kernel weight and dimensions and potentially help breeders in improving kernel weight and architecture-related traits in order to increase wheat yield potential and end-use quality.

Project description:BackgroundPre-harvest sprouting (PHS) is a serious limiting factor for wheat (Triticum aestivum L.) grain yield and end-use quality. Identification of reliable molecular markers and PHS-resistant germplasms is vital to improve PHS resistance by molecular marker-assisted selection (MAS), but the effects of allelic variation and haplotypes in genes conferring PHS resistance in winter wheat cultivars are less understood.ResultsResistance to PHS was tested in 326 commercial winter wheat cultivars for three consecutive growing seasons from 2018-2020. The effects of alleles and haplotypes of 10 genes associated with PHS resistance were determined for all cultivars and were validated by introgressing the PHS-resistance allele and haplotype into a susceptible wheat cultivar. High level of phenotypic variation in PHS resistance was observed in this set of cultivars and 8 of them were highly resistant to PHS with stable germination index (GI) of less than 25% in each individual year. Allelic effects of nine genes and TaMFT haplotype analysis demonstrated that the haplotype Hap1 with low-GI alleles at five positions had the best PHS resistance. This haplotype has the priority to use in improving PHS resistance because of its high effectiveness and rare present in the current commercial cultivars. Among 14 main allelic combinations (ACs) identified, the AC1 carrying the haplotype Hap1 and the TaSdr-B1a allele had better PHS resistance than the other classes. The introgression of Hap1 and TaSdr-B1a is able to significantly improve the PHS resistance in the susceptible cultivar Lunxuan 13.ConclusionsThe effectiveness of alleles conferring PHS resistance in winter wheat cultivars was determined and the useful alleles and haplotypes were identified, providing valuable information for parental selection and MAS aiming at improving PHS-resistance in winter wheat. The identification of the PHS-resistant cultivars without known resistance alleles offers an opportunity to explore new PHS-resistant genes.

Dataset Information

Relationship between Mycotoxin Content in Winter Wheat Grain and Aspirated Dust Collected during Harvest and after Storage.

Publications

Relationship between Mycotoxin Content in Winter Wheat Grain and Aspirated Dust Collected during Harvest and after Storage.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets