A wheat cytochrome P450 enhances both resistance to deoxynivalenol and grain yield.
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ABSTRACT:
SUBMITTER: Gunupuru LR
PROVIDER: S-EPMC6185721 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Deoxynivalenol (DON) is a natural toxin of fungi that cause Fusarium head blight disease of wheat and other small-grain cereals. DON accumulates in infected grains and promotes the spread of the infection on wheat, posing serious problems to grain production. The elucidation of DON-catabolic genes and enzymes in DON-degrading microbes will provide new approaches to decrease DON contamination. Here, we report a cytochrome P450 system capable of catabolizing DON in Sphingomonas sp. strain KSM1, a DON-utilizing bacterium newly isolated from lake water. The P450 gene ddnA was cloned through an activity-based screening of a KSM1 genomic library. The genes of its redox partner candidates (flavin adenine dinucleotide [FAD]-dependent ferredoxin reductase and mitochondrial-type [2Fe-2S] ferredoxin) were not found adjacent to ddnA; the redox partner candidates were further cloned separately based on conserved motifs. The DON-catabolic activity was reconstituted in vitro in an electron transfer chain comprising the three enzymes and NADH, with a catalytic efficiency (k(cat)/K(m)) of 6.4 mM(-1) s(-1). The reaction product was identified as 16-hydroxy-deoxynivalenol. A bioassay using wheat seedlings revealed that the hydroxylation dramatically reduced the toxicity of DON to wheat. The enzyme system showed similar catalytic efficiencies toward nivalenol and 3-acetyl deoxynivalenol, toxins that frequently cooccur with DON. These findings identify an enzyme system that catabolizes DON, leading to reduced phytotoxicity to wheat.
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Project description:Genomic selection (GS) is a strategy to predict the genetic merits of individuals using genome-wide markers. However, GS prediction accuracy is affected by many factors, including missing rate and minor allele frequency (MAF) of genotypic data, GS models, trait features, etc. In this study, we used one wheat population to investigate prediction accuracies of various GS models on yield and yield-related traits from various quality control (QC) scenarios, missing genotype imputation, and genome-wide association studies (GWAS)-derived markers. Missing rate and MAF of single nucleotide polymorphism (SNP) markers were two major factors in QC. Five missing rate levels (0%, 20%, 40%, 60%, and 80%) and three MAF levels (0%, 5%, and 10%) were considered and the five-fold cross validation was used to estimate the prediction accuracy. The results indicated that a moderate missing rate level (20% to 40%) and MAF (5%) threshold provided better prediction accuracy. Under this QC scenario, prediction accuracies were further calculated for imputed and GWAS-derived markers. It was observed that the accuracies of the six traits were related to their heritability and genetic architecture, as well as the GS prediction model. Moore-Penrose generalized inverse (GenInv), ridge regression (RidgeReg), and random forest (RForest) resulted in higher prediction accuracies than other GS models across traits. Imputation of missing genotypic data had marginal effect on prediction accuracy, while GWAS-derived markers improved the prediction accuracy in most cases. These results demonstrate that QC on missing rate and MAF had positive impact on the predictability of GS models. We failed to identify one single combination of QC scenarios that could outperform the others for all traits and GS models. However, the balance between marker number and marker quality is important for the deployment of GS in wheat breeding. GWAS is able to select markers which are mostly related to traits, and therefore can be used to improve the prediction accuracy of GS.
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Project description:Increasing grain yield has always been the primary goal of crop breeding. KLUH/CYP78A5 has been shown to affect seed size in several plant species, but the relevant molecular mechanism is still unclear and there are no reports of this gene contributing to yield. Here, we demonstrate that modified expression of TaCYP78A5 can enhance wheat grain weight and grain yield per plant by accumulating auxin. TaCYP78A5 is highly expressed in maternal tissues, including ovary and seed coat during wheat development. The constitutive overexpression of TaCYP78A5 leads to significantly increased seed size and weight but not grain yield per plant due to the strengthening of apical dominance. However, localized overexpression of TaCYP78A5 in maternal integument enhances grain weight and grain yield per plant by 4.3%-18.8% and 9.6%-14.7%, respectively, in field trials. Transcriptome and hormone metabolome analyses reveal that TaCYP78A5 participates in auxin synthesis pathway and promotes auxin accumulation and cell wall remodelling in ovary. Phenotype investigation and cytological observation show that localized overexpression of TaCYP78A5 in ovary results in delayed flowering and prolonged proliferation of maternal integument cells, which promote grain enlargement. Moreover, naturally occurring variations in the promoter of TaCYP78A5-2A contribute to thousand-grain weight (TGW) and grain yield per plant of wheat;TaCYP78A5-2A haplotype Ap-HapII with higher activity is favourable for improving grain weight and grain yield per plant and has been positively selected in wheat breeding. Then, a functional marker of TaCYP78A5 haplotype Ap-HapII is developed for marker-assisted selection in wheat grain and yield improvement.
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Project description:Grain yield, which is mainly contributed by tillering capacity as well as kernel number and weight, is the most important trait to plant breeders and agronomists. Label-free quantitative proteomics was used to analyse yield-contributing organs in wheat. These were leaf sample, tiller initiation, spike initiation, ovary and three successive kernel development stages at 5, 10 and 15 days after anthesis (DAA). We identified 3182 proteins across all samples. The largest number was obtained for spike initiation (1673), while the smallest was kernel sample at 15 DAA (709). Of the 3182 proteins, 296 of them were common to all seven organs. Organ-specific proteins ranged from 148 in ovary to 561 in spike initiation. When relative protein abundances were compared to that of leaf sample, 347 and 519 proteins were identified as differentially abundant in tiller initiation and spike initiation, respectively. When compared with ovary, 81, 35 and 96 proteins were identified as differentially abundant in kernels sampled at 5, 10 and 15 DAA, respectively. Our study indicated that two Argonaute proteins were solely expressed in spike initiation. Of the four expansin proteins detected, three of them were mainly expressed during the first 10 days of kernel development after anthesis. We also detected cell wall invertases and sucrose and starch synthases mainly during the kernel development period. The manipulation of these proteins could lead to increases in tillers, kernels per spike or final grain weight, and is worth exploring in future studies.
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Project description:To introduce new genetic diversity into the bread wheat gene pool from its progenitor, Aegilops tauschii (Coss.) Schmalh, 33 primary synthetic hexaploid wheat genotypes (SYN) were crossed to 20 spring bread wheat (BW) cultivars at the International Wheat and Maize Improvement Center. Modified single seed descent was used to develop 97 populations with 50 individuals per population using first back-cross, biparental, and three-way crosses. Individuals from each cross were selected for short stature, early heading, flowering and maturity, minimal lodging, and free threshing. Yield trials were conducted under irrigated, drought, and heat-stress conditions from 2011 to 2014 in Ciudad Obregon, Mexico. Genomic estimated breeding values (GEBVs) of parents and synthetic derived lines (SDLs) were estimated using a genomic best linear unbiased prediction (GBLUP) model with markers in each trial. In each environment, there were SDLs that had higher GEBVs than their recurrent BW parent for yield. The GEBVs of BW parents for yield ranged from -0.32 in heat to 1.40 in irrigated trials. The range of the SYN parent GEBVs for yield was from -2.69 in the irrigated to 0.26 in the heat trials and were mostly negative across environments. The contribution of the SYN parents to improved grain yield of the SDLs was highest under heat stress, with an average GEBV for the top 10% of the SDLs of 0.55 while the weighted average GEBV of their corresponding recurrent BW parents was 0.26. Using the pedigree-based model, the accuracy of genomic prediction for yield was 0.42, 0.43, and 0.49 in the drought, heat and irrigated trials, respectively, while for the marker-based model these values were 0.43, 0.44, and 0.55. The SYN parents introduced novel diversity into the wheat gene pool. Higher GEBVs of progenies were due to introgression and retention of some positive alleles from SYN parents.
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Project description:One-step and two-step pathways are proposed to synthesize cytokinin. One-step pathway is mediated by LONELY GUY (LOG) proteins. However, the enzyme for two-step pathway has not been identified yet. Here, we show that a QTL GY3 boosts grain yield by more than 20% via manipulating two-step pathway. GY3 encodes a LOG protein that acts as a 5’-ribonucleotide phosphohydrolase by excessive consuming the cytokinin precursors, which contrasts with the activity of canonical LOG members as phosphoribohydrolases in a one-step pathway. The residue S41 of GY3 is crucial for the dephosphorylation of iPRMP. A solo-LTR insertion within the promoter of GY3 suppressed its expression and resulted in a higher content of active cytokinins in young panicles. Introgression of GY302428 increased grain yield per plot by 7.4% to 16.3% in all investigated indica backgrounds both under long-day and short-day conditions, which demonstrates the great value of GY302428 in indica rice production.
2023-05-05 | GSE231360 | GEO
Project description:BACKGROUND:Modern agriculture uses hyperspectral cameras to obtain hundreds of reflectance data measured at discrete narrow bands to cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra, depending on the camera. This information is used to construct vegetation indices (VI) (e.g., green normalized difference vegetation index or GNDVI, simple ratio or SRa, etc.) which are used for the prediction of primary traits (e.g., biomass). However, these indices only use some bands and are cultivar-specific; therefore they lose considerable information and are not robust for all cultivars. RESULTS:This study proposes models that use all available bands as predictors to increase prediction accuracy; we compared these approaches with eight conventional vegetation indexes (VIs) constructed using only some bands. The data set we used comes from CIMMYT's global wheat program and comprises 1170 genotypes evaluated for grain yield (ton/ha) in five environments (Drought, Irrigated, EarlyHeat, Melgas and Reduced Irrigated); the reflectance data were measured in 250 discrete narrow bands ranging between 392 and 851 nm. The proposed models for the simultaneous analysis of all the bands were ordinal least square (OLS), Bayes B, principal components with Bayes B, functional B-spline, functional Fourier and functional partial least square. The results of these models were compared with the OLS performed using as predictors each of the eight VIs individually and combined. CONCLUSIONS:We found that using all bands simultaneously increased prediction accuracy more than using VI alone. The Splines and Fourier models had the best prediction accuracy for each of the nine time-points under study. Combining image data collected at different time-points led to a small increase in prediction accuracy relative to models that use data from a single time-point. Also, using bands with heritabilities larger than 0.5 only in Drought as predictor variables showed improvements in prediction accuracy.
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Project description:The current perspective of increasing global temperature makes heat stress as a major threat to wheat production worldwide. In order to identify quantitative trait loci (QTLs) associated with heat tolerance, 251 recombinant inbred lines (RILs) derived from a cross between HD2808 (heat tolerant) and HUW510 (heat susceptible) were evaluated under timely sown (normal) and late sown (heat stress) conditions for two consecutive crop seasons; 2013-14 and 2014-15. Grain yield (GY) and its components namely, grain weight/spike (GWS), grain number/spike (GNS), thousand grain weight (TGW), grain filling rate (GFR) and grain filling duration (GFD) were recorded for both conditions and years. The data collected for both timely and late sown conditions and heat susceptibility index (HSI) of these traits were used as phenotypic data for QTL identification. The frequency distribution of HSI for all the studied traits was continuous during both the years and also included transgressive segregants. Composite interval mapping identified total 24 QTLs viz., 9 (timely sown traits), 6 (late sown traits) and 9 (HSI of traits) mapped on linkage groups 2A, 2B, and 6D during both the crop seasons 2013-14 and 2014-15. The QTLs were detected for GWS (6), GNS (6), GFR (4), TGW (3), GY (3) and GFD (2). The LOD score of identified QTLs varied from 3.03 (Qtgns.iiwbr-6D) to 21.01 (Qhsitgw.iiwbr-2A) during 2014-15, explaining 11.2 and 30.6% phenotypic variance, respectively. Maximum no of QTLs were detected in chromosome 2A followed by 6D and 2B. All the QTL detected under late sown and HSI traits were identified on chromosome 2A except for QTLs associated with GFD. Fifteen out of 17 QTL detected on chromosome 2A were clustered within the marker interval between gwm448 and wmc296 and showed tight linkage with gwm122 and these were localized in 49-52 cM region of Somers consensus map of chromosome 2A i.e. within 18-59.56 cM region of chromosome 2A where no QTL related to heat stress were reported earlier. Besides, three consistent QTLs, Qgws.iiwbr-2A, Qgns.iiwbr-2A and Qgns.iiwbr-2A were also detected in all the environments in this region. The nearest QTL detected in earlier studies, QFv/Fm.cgb-2A was approximately 6cM below the presently identified QTLs region, respectively Additionally, QTLs for physiological and phenological traits and plant height under late sown and HSI of these traits were also detected on chromosome 2A. QTL for HSI of plant height and physiological maturity were located in the same genomic region of chromosome 2Awhereas QTLs for physiological and phonological traits under late sown were located 8cM and 33.5 cM below the genomic location associated with grain traits, respectively in consensus map of Somers. This QTL hot-spot region with consistent QTLs could be used to improve heat tolerance after validation.
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