Metabolic engineering of astaxanthin biosynthesis in maize endosperm and characterization of a prototype high oil hybrid

Maize was genetically engineered for the biosynthesis of the high value carotenoid astaxanthin in the kernel endosperm. Introduction of a β-carotene hydroxylase and a β-carotene ketolase into a white maize genetic background extended the carotenoid pathway to astaxanthin. Simultaneously, phytoene synthase, the controlling enzyme of carotenogenesis, was over-expressed for enhanced carotenoid production and lycopene ε-cyclase was knocked-down to direct more precursors into the β-branch of the extended ketocarotenoid pathway which ends with astaxanthin. This astaxanthin-accumulating transgenic line was crossed into a high oil- maize genotype in order to increase the storage capacity for lipophilic astaxanthin. The high oil astaxanthin hybrid was compared to its astaxanthin producing parent. We report an in depth metabolomic and proteomic analysis which revealed major up- or down- regulation of genes involved in primary metabolism. Specifically, amino acid biosynthesis and the citric acid cycle which compete with the synthesis or utilization of pyruvate and glyceraldehyde 3-phosphate, the precursors for carotenogenesis, were down-regulated. Nevertheless, principal component analysis demonstrated that this compositional change is within the range of the two wild type parents used to generate the high oil producing astaxanthin hybrid

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

Structural journey of an insecticidal protein against western corn rootworm

The broad adoption of transgenic crops has revolutionized agriculture. However, resistance to insecticidal proteins by agricultural pests poses a continuous challenge to maintaining crop productivity and new proteins are urgently needed to replace those utilized for existing transgenic traits. We identified an insecticidal membrane attack complex/perforin (MACPF) protein, Mpf2Ba1, with strong activity against the devastating coleopteran pest western corn rootworm (WCR) and a novel site of action. Using an integrative structural biology approach, we determined monomeric, pre-pore and pore structures, revealing changes between structural states at high resolution. We discovered an assembly inhibition mechanism, a molecular switch that activates pre-pore oligomerization upon gut fluid incubation and solved the highest resolution MACPF pore structure to-date. Our findings demonstrate not only the utility of Mpf2Ba1 in the development of biotechnology solutions for protecting maize from WCR to promote food security, but also uncover previously unknown mechanistic principles of bacterial MACPF assembly

Measurement of Single Soybean Seed Attributes by Near-Infrared Technologies. A Comparative Study

Four near-infrared spectrophotometers, and their associated spectral collection methods, were tested and compared for measuring three soybean single-seed attributes: weight (g), protein (%), and oil (%). Using partial least-squares (PLS) and four preprocessing methods, the attribute that was significantly most easily predicted was seed weight (RPD > 3 on average) and protein the least. The performance of all instruments differed from each other. Performances for oil and protein predictions were correlated with the instrument sampling system, with the best predictions using spectra taken from more than one seed angle. This was facilitated by the seed spinning or tumbling during spectral collection as opposed to static sampling methods. From the preprocessing methods utilized, no single one gave the best overall performances but weight measurements were often more successful with raw spectra, whereas protein and oil predictions were often enhanced by SNV and SNV + detrending.Posted with permission from Journal of Agricultural and Food Chemistry 60 (2012): 8314–8322, doi:10.1021/jf3012807. Copyright 2012 American Chemical Society.</p