A foundation for provitamin A biofortification of maize: genome-wide association and genomic prediction models of carotenoid levels.

Efforts are underway for development of crops with improved levels of provitamin A carotenoids to help combat dietary vitamin A deficiency. As a global staple crop with considerable variation in kernel carotenoid composition, maize (Zea mays L.) could have a widespread impact. We performed a genome-wide association study (GWAS) of quantified seed carotenoids across a panel of maize inbreds ranging from light yellow to dark orange in grain color to identify some of the key genes controlling maize grain carotenoid composition. Significant associations at the genome-wide level were detected within the coding regions of zep1 and lut1, carotenoid biosynthetic genes not previously shown to impact grain carotenoid composition in association studies, as well as within previously associated lcyE and crtRB1 genes. We leveraged existing biochemical and genomic information to identify 58 a priori candidate genes relevant to the biosynthesis and retention of carotenoids in maize to test in a pathway-level analysis. This revealed dxs2 and lut5, genes not previously associated with kernel carotenoids. In genomic prediction models, use of markers that targeted a small set of quantitative trait loci associated with carotenoid levels in prior linkage studies were as effective as genome-wide markers for predicting carotenoid traits. Based on GWAS, pathway-level analysis, and genomic prediction studies, we outline a flexible strategy involving use of a small number of genes that can be selected for rapid conversion of elite white grain germplasm, with minimal amounts of carotenoids, to orange grain versions containing high levels of provitamin A

Population genomics of Drosophila suzukii reveal longitudinal population structure and signals of migrations in and out of the continental United States

Drosophila suzukii, or spotted-wing drosophila, is now an established pest in many parts of the world, causing significant damage to numerous fruit crop industries. Native to East Asia, D. suzukii infestations started in the United States (U.S.) a decade ago, occupying a wide range of climates. To better understand invasion ecology of this pest, knowledge of past migration events, population structure, and genetic diversity is needed. In this study, we sequenced whole genomes of 237 individual flies collected across the continental U.S., as well as several sites in Europe, Brazil, and Asia, to identify and analyze hundreds of thousands of genetic markers. We observed strong population structure between Western and Eastern U.S. populations, but no evidence of any population structure between different latitudes within the continental U.S., suggesting there is no broad-scale adaptations occurring in response to differences in winter climates. We detect admixture from Hawaii to the Western U.S. and from the Eastern U.S. to Europe, in agreement with previously identified introduction routes inferred from microsatellite analysis. We also detect potential signals of admixture from the Western U.S. back to Asia, which could have important implications for shipping and quarantine policies for exported agriculture. We anticipate this large genomic dataset will spur future research into the genomic adaptations underlying D. suzukii pest activity and development of novel control methods for this agricultural pes

Peptidergic control in a fruit crop pest: The spotted-wing drosophila, Drosophila suzukii

Neuropeptides play an important role in the regulation of feeding in insects and offer potential targets for the development of new chemicals to control insect pests. A pest that has attracted much recent attention is the highly invasive Drosophila suzukii, a polyphagous pest that can cause serious economic damage to soft fruits. Previously we showed by mass spectrometry the presence of the neuropeptide myosuppressin (TDVDHVFLRFamide) in the nerve bundle suggesting that this peptide is involved in regulating the function of the crop, which in adult dipteran insects has important roles in the processing of food, the storage of carbohydrates and the movement of food into the midgut for digestion. In the present study antibodies that recognise the C-terminal RFamide epitope of myosuppressin stain axons in the crop nerve bundle and reveal peptidergic fibres covering the surface of the crop. We also show using an in vitro bioassay that the neuropeptide is a potent inhibitor (EC50 of 2.3 nM) of crop contractions and that this inhibition is mimicked by the non-peptide myosuppressin agonist, benzethonium chloride (Bztc). Myosuppressin also inhibited the peristaltic contractions of the adult midgut, but was a much weaker agonist (EC50 = 5.7 μM). The oral administration of Bztc (5 mM) in a sucrose diet to adult female D. suzukii over 4 hours resulted in less feeding and longer exposure to dietary Bztc led to early mortality. We therefore suggest that myosuppressin and its cognate receptors are potential targets for disrupting feeding behaviour of adult D. suzukii

Optimizing the procedure of grain nutrient predictions in barley via hyperspectral imaging

Hyperspectral imaging enables researchers and plant breeders to analyze various traits of interest like nutritional value in high throughput. In order to achieve this, the optimal design of a reliable calibration model, linking the measured spectra with the investigated traits, is necessary. In the present study we investigated the impact of different regression models, calibration set sizes and calibration set compositions on prediction performance. For this purpose, we analyzed concentrations of six globally relevant grain nutrients of the wild barley population HEB-YIELD as case study. The data comprised 1,593 plots, grown in 2015 and 2016 at the locations Dundee and Halle, which have been entirely analyzed through traditional laboratory methods and hyperspectral imaging. The results indicated that a linear regression model based on partial least squares outperformed neural networks in this particular data modelling task. There existed a positive relationship between the number of samples in a calibration model and prediction performance, with a local optimum at a calibration set size of ~40% of the total data. The inclusion of samples from several years and locations could clearly improve the predictions of the investigated nutrient traits at small calibration set sizes. It should be stated that the expansion of calibration models with additional samples is only useful as long as they are able to increase trait variability. Models obtained in a certain environment were only to a limited extent transferable to other environments. They should therefore be successively upgraded with new calibration data to enable a reliable prediction of the desired traits. The presented results will assist the design and conceptualization of future hyperspectral imaging projects in order to achieve reliable predictions. It will in general help to establish practical applications of hyperspectral imaging systems, for instance in plant breeding concepts

CHARACTERIZATION OF REFINED HEMP FIBERS USING NIR FT RAMAN MICRO SPECTROSCOPY AND ENVIRONMENTAL SCANNING ELECTRON MICROSCOPY

The research was focused on the separation of single hemp (Cannabis sativa L.) fibre cells with low fineness from mechanically extracted fibre bundles of high fineness. The fiber bundles were treated with enzymes, namely panzym, pectinase, hemicellulase, and cellulase, along with a combination of panzym and ultrasonic treatments. Changes in the fiber structure were followed at molecular and microscopic levels by means of NIR FT Raman spectroscopy and Environmental Scanning Electron Microscopy (ESEM). Buffer-panzym treatments of hemp fibers had a prominent effect in loosening of the fiber cells. The best of refining was achieved when the fiber bundles were treated with buffer-panzym solution in combination with ultrasonic treatment

Correction to: High-throughput method for ear phenotyping and kernel weight estimation in maize using ear digital imaging

[This corrects the article DOI: 10.1186/s13007-018-0317-4.]

High-throughput method for ear phenotyping and kernel weight estimation in maize using ear digital imaging

BackgroundGrain yield, ear and kernel attributes can assist to understand the performance of maize plant under different environmental conditions and can be used in the variety development process to address farmer's preferences. These parameters are however still laborious and expensive to measure.ResultsA low-cost ear digital imaging method was developed that provides estimates of ear and kernel attributes i.e., ear number and size, kernel number and size as well as kernel weight from photos of ears harvested from field trial plots. The image processing method uses a script that runs in a batch mode on ImageJ; an open source software. Kernel weight was estimated using the total kernel number derived from the number of kernels visible on the image and the average kernel size. Data showed a good agreement in terms of accuracy and precision between ground truth measurements and data generated through image processing. Broad-sense heritability of the estimated parameters was in the range or higher than that for measured grain weight. Limitation of the method for kernel weight estimation is discussed.ConclusionThe method developed in this work provides an opportunity to significantly reduce the cost of selection in the breeding process, especially for resource constrained crop improvement programs and can be used to learn more about the genetic bases of grain yield determinants