Statistical sampling of missing environmental variables improves biophysical genomic prediction in wheat.

KEY MESSAGE: The integration of genomic prediction with crop growth models enabled the estimation of missing environmental variables which improved the prediction accuracy of grain yield. Since the invention of whole-genome prediction (WGP) more than two decades ago, breeding programmes have established extensive reference populations that are cultivated under diverse environmental conditions. The introduction of the CGM-WGP model, which integrates crop growth models (CGM) with WGP, has expanded the applications of WGP to the prediction of unphenotyped traits in untested environments, including future climates. However, CGMs require multiple seasonal environmental records, unlike WGP, which makes CGM-WGP less accurate when applied to historical reference populations that lack crucial environmental inputs. Here, we investigated the ability of CGM-WGP to approximate missing environmental variables to improve prediction accuracy. Two environmental variables in a wheat CGM, initial soil water content (InitlSoilWCont) and initial nitrate profile, were sampled from different normal distributions separately or jointly in each iteration within the CGM-WGP algorithm. Our results showed that sampling InitlSoilWCont alone gave the best results and improved the prediction accuracy of grain number by 0.07, yield by 0.06 and protein content by 0.03. When using the sampled InitlSoilWCont values as an input for the traditional CGM, the average narrow-sense heritability of the genotype-specific parameters (GSPs) improved by 0.05, with GNSlope, PreAnthRes, and VernSen showing the greatest improvements. Moreover, the root mean square of errors for grain number and yield was reduced by about 7% for CGM and 31% for CGM-WGP when using the sampled InitlSoilWCont values. Our results demonstrate the advantage of sampling missing environmental variables in CGM-WGP to improve prediction accuracy and increase the size of the reference population by enabling the utilisation of historical data that are missing environmental records.

Hard-to-cook phenomenon in common legumes: Chemistry, mechanisms and utilisation.

Future dietary protein demand will focus more on plant-based sources than animal-based products. In this scenario, legumes and pulses (lentils, beans, chickpeas, etc.) can play a crucial role as they are one of the richest sources of plant proteins with many health benefits. However, legume consumption is undermined due to the hard-to-cook (HTC) phenomenon, which refers to legumes that have high resistance to softening during cooking. This review provides mechanistic insight into the development of the HTC phenomenon in legumes with a special focus on common beans and their nutrition, health benefits, and hydration behaviour. Furthermore, detailed elucidation of HTC mechanisms, mainly pectin-cation-phytate hypothesis and compositional changes of macronutrients like starch, protein, lipids and micronutrients like minerals, phytochemicals and cell wall polysaccharides during HTC development are critically reviewed based on the current research findings. Finally, strategies to improve the hydration and cooking quality of beans are proposed, and a perspective is provided.

Single Seed Near-Infrared Hyperspectral Imaging for Classification of Perennial Ryegrass Seed.

The detection of beneficial microbes living within perennial ryegrass seed causing no apparent defects is challenging, even with the most sensitive and conventional methods, such as DNA genotyping. Using a near-infrared hyperspectral imaging system (NIR-HSI), we were able to discriminate not only the presence of the commercial NEA12 fungal endophyte strain but perennial ryegrass cultivars of diverse seed age and batch. A total of 288 wavebands were extracted for individual seeds from hyperspectral images. The optimal pre-processing methods investigated yielded the best partial least squares discriminant analysis (PLS-DA) classification model to discriminate NEA12 and without endophyte (WE) perennial ryegrass seed with a classification accuracy of 89%. Effective wavelength (EW) selection based on GA-PLS-DA resulted in the selection of 75 wavebands yielding 88.3% discrimination accuracy using PLS-DA. For cultivar identification, the artificial neural network discriminant analysis (ANN-DA) was the best-performing classification model, resulting in >90% classification accuracy for Trojan, Alto, Rohan, Governor and Bronsyn. EW selection using GA-PLS-DA resulted in 87 wavebands, and the PLS-DA model performed the best, with no extensive compromise in performance, resulting in >89.1% accuracy. The study demonstrates the use of NIR-HSI reflectance data to discriminate, for the first time, an associated beneficial fungal endophyte and five cultivars of perennial ryegrass seed, irrespective of seed age and batch. Furthermore, the negligible effects on the classification errors using EW selection improve the capability and deployment of optimized methods for real-time analysis, such as the use of low-cost multispectral sensors for single seed analysis and automated seed sorting devices.

Using genomic prediction with crop growth models enables the prediction of associated traits in wheat.

Crop growth models (CGM) can predict the performance of a cultivar in untested environments by sampling genotype-specific parameters. As they cannot predict the performance of new cultivars, it has been proposed to integrate CGMs with whole genome prediction (WGP) to combine the benefits of both models. Here, we used a CGM-WGP model to predict the performance of new wheat (Triticum aestivum) genotypes. The CGM was designed to predict phenology, nitrogen, and biomass traits. The CGM-WGP model simulated more heritable GSPs compared with the CGM and gave smaller errors for the observed phenotypes. The WGP model performed better when predicting yield, grain number, and grain protein content, but showed comparable performance to the CGM-WGP model for heading and physiological maturity dates. However, the CGM-WGP model was able to predict unobserved traits (for which there were no phenotypic records in the reference population). The CGM-WGP model also showed superior performance when predicting unrelated individuals that clustered separately from the reference population. Our results demonstrate new advantages for CGM-WGP modelling and suggest future efforts should focus on calibrating CGM-WGP models using high-throughput phenotypic measures that are cheaper and less laborious to collect.

A Simple High-Throughput Method for the Analysis of Vicine and Convicine in Faba Bean.

The faba bean is one of the earliest domesticated crops, with both economic and environmental benefits. Like most legumes, faba beans are high in protein, and can be used to contribute to a balanced diet, or as a meat substitute. However, they also produce the anti-nutritional compounds, vicine and convicine (v-c), that when enzymatically degraded into reactive aglycones can potentially lead to hemolytic anemia or favism. Current methods of analysis use LC-UV, but are only suitable at high concentrations, and thus lack the selectivity and sensitivity to accurately quantitate the low-v-c genotypes currently being developed. We have developed and fully validated a rapid high-throughput LC-MS method for the analysis of v-c in faba beans by optimizing the extraction protocol and assessing the method of linearity, limit of detection, limit of quantitation, accuracy, precision and matrix effects. This method uses 10-times less starting material; removes the use of buffers, acids and organic chemicals; and improves precision and accuracy when compared to current methods.

Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview.

Near-infrared (800-2500 nm; NIR) spectroscopy coupled to hyperspectral imaging (NIR-HSI) has greatly enhanced its capability and thus widened its application and use across various industries. This non-destructive technique that is sensitive to both physical and chemical attributes of virtually any material can be used for both qualitative and quantitative analyses. This review describes the advancement of NIR to NIR-HSI in agricultural applications with a focus on seed quality features for agronomically important seeds. NIR-HSI seed phenotyping, describing sample sizes used for building high-accuracy calibration and prediction models for full or selected wavelengths of the NIR region, is explored. The molecular interpretation of absorbance bands in the NIR region is difficult; hence, this review offers important NIR absorbance band assignments that have been reported in literature. Opportunities for NIR-HSI seed phenotyping in forage grass seed are described and a step-by-step data-acquisition and analysis pipeline for the determination of seed quality in perennial ryegrass seeds is also presented.

Mining the Wheat Grain Proteome.

Bread wheat is the most widely cultivated crop worldwide, used in the production of food products and a feed source for animals. Selection tools that can be applied early in the breeding cycle are needed to accelerate genetic gain for increased wheat production while maintaining or improving grain quality if demand from human population growth is to be fulfilled. Proteomics screening assays of wheat flour can assist breeders to select the best performing breeding lines and discard the worst lines. In this study, we optimised a robust LC-MS shotgun quantitative proteomics method to screen thousands of wheat genotypes. Using 6 cultivars and 4 replicates, we tested 3 resuspension ratios (50, 25, and 17 µL/mg), 2 extraction buffers (with urea or guanidine-hydrochloride), 3 sets of proteases (chymotrypsin, Glu-C, and trypsin/Lys-C), and multiple LC settings. Protein identifications by LC-MS/MS were used to select the best parameters. A total 8738 wheat proteins were identified. The best method was validated on an independent set of 96 cultivars and peptides quantities were normalised using sample weights, an internal standard, and quality controls. Data mining tools found particularly useful to explore the flour proteome are presented (UniProt Retrieve/ID mapping tool, KEGG, AgriGO, REVIGO, and Pathway Tools).

A community resource to mass explore the wheat grain proteome and its application to the late-maturity alpha-amylase (LMA) problem.

BACKGROUND: Late-maturity alpha-amylase (LMA) is a wheat genetic defect causing the synthesis of high isoelectric point alpha-amylase following a temperature shock during mid-grain development or prolonged cold throughout grain development, both leading to starch degradation. While the physiology is well understood, the biochemical mechanisms involved in grain LMA response remain unclear. We have applied high-throughput proteomics to 4,061 wheat flours displaying a range of LMA activities. Using an array of statistical analyses to select LMA-responsive biomarkers, we have mined them using a suite of tools applicable to wheat proteins. RESULTS: We observed that LMA-affected grains activated their primary metabolisms such as glycolysis and gluconeogenesis; TCA cycle, along with DNA- and RNA- binding mechanisms; and protein translation. This logically transitioned to protein folding activities driven by chaperones and protein disulfide isomerase, as well as protein assembly via dimerisation and complexing. The secondary metabolism was also mobilized with the upregulation of phytohormones and chemical and defence responses. LMA further invoked cellular structures, including ribosomes, microtubules, and chromatin. Finally, and unsurprisingly, LMA expression greatly impacted grain storage proteins, as well as starch and other carbohydrates, with the upregulation of alpha-gliadins and starch metabolism, whereas LMW glutenin, stachyose, sucrose, UDP-galactose, and UDP-glucose were downregulated. CONCLUSIONS: To our knowledge, this is not only the first proteomics study tackling the wheat LMA issue but also the largest plant-based proteomics study published to date. Logistics, technicalities, requirements, and bottlenecks of such an ambitious large-scale high-throughput proteomics experiment along with the challenges associated with big data analyses are discussed.

High-Throughput Analysis of Amino Acids for Protein Quantification in Plant and Animal-Derived Samples Using High Resolution Mass Spectrometry.

Current methods for measuring the abundance of proteogenic amino acids in plants require derivatisation, extended run times, very sensitive pH adjustments of the protein hydrolysates, and the use of buffers in the chromatographic phases. Here, we describe a fast liquid chromatography-mass spectrometry (LC-MS) method for the determination of amino acids that requires only three steps: hydrolysis, neutralisation, and sample dilution with a borate buffer solution for pH and retention time stability. The method shows excellent repeatability (repeated consecutive injections) and reproducibility (repeated hydrolysis) in the amino acid content, peak area, and retention time for all the standard amino acids. The chromatographic run time is 20 min with a reproducibility and repeatability of <1% for the retention time and <11% for the peak area of the BSA and quality control (QC) lentil samples. The reproducibility of the total protein levels in the hydrolysis batches 1-4 was <12% for the BSA and the lentil samples. The level of detection on column was below 0.1 µM for most amino acids (mean 0.017 µM).

Genomic Prediction and Genetic Correlation of Agronomic, Blackleg Disease, and Seed Quality Traits in Canola (Brassica napus L.).

Genomic selection accelerates genetic progress in crop breeding through the prediction of future phenotypes of selection candidates based on only their genomic information. Here we report genetic correlations and genomic prediction accuracies in 22 agronomic, disease, and seed quality traits measured across multiple years (2015-2017) in replicated trials under rain-fed and irrigated conditions in Victoria, Australia. Two hundred and two spring canola lines were genotyped for 62,082 Single Nucleotide Polymorphisms (SNPs) using transcriptomic genotype-by-sequencing (GBSt). Traits were evaluated in single trait and bivariate genomic best linear unbiased prediction (GBLUP) models and cross-validation. GBLUP were also expanded to include genotype-by-environment G × E interactions. Genomic heritability varied from 0.31to 0.66. Genetic correlations were highly positive within traits across locations and years. Oil content was positively correlated with most agronomic traits. Strong, not previously documented, negative correlations were observed between average internal infection (a measure of blackleg disease) and arachidic and stearic acids. The genetic correlations between fatty acid traits followed the expected patterns based on oil biosynthesis pathways. Genomic prediction accuracy ranged from 0.29 for emergence count to 0.69 for seed yield. The incorporation of G × E translates into improved prediction accuracy by up to 6%. The genomic prediction accuracies achieved indicate that genomic selection is ready for application in canola breeding.

Improved Wheat Growth and Yield by Delayed Leaf Senescence Using Developmentally Regulated Expression of a Cytokinin Biosynthesis Gene.

Delaying leaf senescence in plants, especially under water stress conditions, can help to maintain the remobilization of stored nutrients in source-sink relationships, thus leading to improved crop yields. Leaf senescence can be delayed by plant hormones such as cytokinin. Here, the Isopentenyl transferase (IPT) gene, encoding a cytokinin biosynthesis enzyme, driven by a modified AtMYB32xs promoter was transformed into wheat. Transgenic wheat plants exhibited delayed leaf senescence, retaining chlorophyll for longer under controlled environment conditions. Selected independent transgenic events and their corresponding nulls were grown under field conditions for two consecutive years under well-watered and water stress treatments using automated rainout shelters. Three independent transgenic events had improved canopy green cover, lower canopy temperatures, and higher leaf water potential than their respective non-transgenic nulls, with no abnormality in morphology and phenology. Increased grain yield was observed in transgenic events under both water treatments, with the yield increase more pronounced under water stress (26-42%). These results have shown that delayed leaf senescence using the chimeric transgene AtMYB32xs-p::IPT can be a useful strategy to achieve grain yield gains in wheat and potentially other crops for sustainable food production.

NMR Based Metabolomic Analysis of Health Promoting Phytochemicals in Lentils.

Lentils are a high-protein plant food and a valuable source of human nutrition, particularly in the Indian subcontinent. However, beyond sustenance, there is evidence that the consumption of lentils (and legumes in general) is associated with decreased risk of diseases, such as diabetes and cardiovascular disease. Lentils contain health-promoting phytochemicals, such as trigonelline and various polyphenolics. Fourteen lentil genotypes were grown at three locations to explore the variation in phytochemical composition in hulls and cotyledons. Significant differences were measured between genotypes and environments, with some genotypes more affected by environment than others. However, there was a strong genetic effect which indicated that future breeding programs could breed for lentils that product more of these health-promoting phytochemicals.

Genotype and environment effects on the chemical composition and rheological properties of field peas.

BACKGROUND: The inclusion of pulses in traditional wheat-based food products such as bread, cakes, and pasta is increasing as the food industry and consumers are recognizing the nutritional benefits due to high protein, antioxidant activity, and good sources of dietary fiber of pulses. In all crops, including cereals, oilseeds, and pulses, variability in chemical composition is known to exist due to genetic differences and environmental effects. This study reports the effect of genotype and environment on seed composition and the rheological properties of field-pea genotypes for both field-pea flour and isolated starch. RESULTS: Genotype had a significant effect on the chemical composition (protein, total starch, water-soluble carbohydrates, and phenolic compounds), the mean starch granule size, and rheological properties (peak viscosity, breakdown viscosity, final viscosity, peak time, and pasting temperature) of the field peas. The growing environment also had a significant effect on starch granule size, phytic acid, water-soluble carbohydrates, some phenolic compounds, and pasting characteristics of field peas. Genotype × environment (G × E) interactions were observed for protein, some phenolic compounds, and some pasting characteristics. CONCLUSION: Genotype and the growing environment had a significant effect on the chemical composition and rheological properties of the field pea. The variability in composition and quality traits could be exploited through plant breeding and optimized agronomic practices to increase production of field peas with the desired quality traits. © 2019 Society of Chemical Industry.

Annual ryegrass (Lolium rigidum Gaud) competition altered wheat grain quality: A study under elevated atmospheric CO2 levels and drought conditions.

Annual ryegrass is one of the most serious, costly weeds of winter cropping systems in Australia. To determine whether its competition-mediated plant defence mechanisms effect on wheat grain quality, wheat (cv. Yitpi) and annual ryegrass were grown under two levels of CO2 (400 ppm; (a[CO2]) vs 700 ppm; (e[CO2]), two levels of water (well-watered vs drought) and two types of competition (wheat only; (W), and wheat × annual ryegrass; (W × R) with four replicates. The competition × [CO2] interaction had a significant effect on wheat grain protein content, where it was increased in W × R under both e[CO2] (+17%) and a[CO2] (+21%). Grain yield, total grain reducing power and phenolic content were significantly affected by [CO2] × drought × competition. In a summary, annual ryegrass competition significantly altered the wheat grain quality under both [CO2] levels (depending on the soil water level), while also decreasing the grain yield.

Bioaccessibility of Some Essential Minerals in Three Selected Australian Pulse Varieties Using an In Vitro Gastrointestinal Digestion Model.

Australian produced pulse grains are exported worldwide, predominantly to developing countries where severe essential mineral deficiencies putatively subsist. An in vitro digestion model that simulates human gastric, intestinal and colonic digestion and fermentation, was used to examine the bioaccessibility of Fe, Mg, K, Ca, P, Zn, Mn, and Cu in commercially available cultivars of Australian field pea, lentil, and sweet lupin. The hull and dehulled seeds were prepared following a traditional cooking method, and quantities of bioaccessible minerals were assessed at each stage of in vitro digestion using ICP-OES elemental analyses. Results revealed that dehulled field pea (100 g dry weight) had the highest bioaccessible quantity of Fe (2.44 ± 0.73 mg), K (717.10 ± 56.66 mg), P (272.88 ± 9.30 mg), Zn (1.72.028 ± 0.28 mg), and Cu (0.41 ± 0.02 mg). Dehulled lupin was the best source of Mg (138.62 ± 1.53 mg) and Mn (1.28 ± 0.0.06 mg), and lentil hull showed the greatest Ca bioaccessible quantity (116.33 ± 16.73 mg/100 g dry weight). Additionally, the fed state digestion (11.7 mg bile/mL sample) increased the bioaccessibility of all elements significantly (P < 0.05) compared to fasted (1.95 mg bile/mL sample), except for Zn and Mn in lupin and lentils. These results demonstrated that dehulled seeds possess higher mineral bioaccessibility on a percentage basis compared with hulls, and that the fed state of in vitro digestion generally improved the mineral solubility significantly (P < 0.05). PRACTICAL APPLICATION: This research aimed to assess the prospective biological accessibility of various essential elements in three commercially available Australian pulses. Results of the study provided an insight into the contents of essential minerals in Australian pulses and illustrated the impact of traditional cooking of dehulled pulses on these minerals bioaccessibility. These findings will provide the consumers with information about some nutritional aspects of major Australian pulses.

Rising CO2 concentration altered wheat grain proteome and flour rheological characteristics.

Wheat cv. H45 was grown under ambient CO2 concentration and Free Air CO2 Enrichment (FACE; e[CO2], ∼550 µmol CO2 mol(-1)). The effect of FACE on wheat grain proteome and associated changes in the flour rheological properties was investigated. A comparative proteomic analysis was performed using 2-D-DIGE followed by MALDI/TOF-MS. Total grain protein concentration was decreased by 9% at e[CO2]. Relative abundance of three high molecular weight glutenin sub units (HMW-GS) were decreased at e[CO2]. In contrast, relative abundance of serpins Z1C and 1-Cys peroxiredoxin was increased at e[CO2]. Elevated [CO2] also decreased the bread volume (by 11%) and dough strength (by 7%) while increased mixing time. However, dough extensibility and dough stability were unchanged at elevated [CO2]. These findings suggest that e[CO2] has a major impact on gluten protein concentration which is associated lower bread quality at e[CO2].

The detection of QTLs in barley associated with endosperm hardness, grain density, grain size and malting quality using rapid phenotyping tools.

Using a barley mapping population, 'Vlamingh' × 'Buloke' (V × B), whole grain analyses were undertaken for physical seed traits and malting quality. Grain density and size were predicted by digital image analysis (DIA), while malt extract and protein content were predicted using near infrared (NIR) analysis. Validation of DIA and NIR algorithms confirmed that data for QTL analysis was highly correlated (R (2) > 0.82), with high RPD values (the ratio of the standard error of prediction to the standard deviation, 2.31-9.06). Endosperm hardness was measured on this mapping population using the single kernel characterisation system. Grain density and endosperm hardness were significantly inter-correlated in all three environments (r > 0.22, P < 0.001); however, other grain components were found to interact with the traits. QTL for these traits were also found on different genomic regions, for example, grain density QTLs were found on chromosomes 2H and 6H, whereas endosperm hardness QTLs were found on 1H, 5H, and 7H. In this study, the majority of the genomic regions associated with grain texture were also coincident with QTLs for grain size, yield, flowering date and/or plant development genes. This study highlights the complexity of genomic regions associated with the variation of endosperm hardness and grain density, and their relationships with grain size traits, agronomic-related traits, and plant development loci.

Class targeted metabolomics: ESI ion trap screening methods for glucosinolates based on MSn fragmentation.

Glucosinolates are naturally occurring anionic secondary plant metabolites incorporating a thioglucosidic link to the carbon of a sulphonated oxime. There are a large number of naturally occurring glucosinolates and they are found in relatively large quantities in many plant species within the family Crucifereae. These metabolites are of interest for both their anticancer and flavour properties and in the study of nitrogen and sulphur metabolism in model plants such as Arabidopsis. Parent ion mapping is an analytical mass spectrometry approach that allows rapid assessment of glucosinolate content. Ion mapping proved to be highly sensitive and the glucosinolate sinigrin could be detected at three parts per trillion. This method takes advantage of the glucosinolate anion fragmentation which consistently produces a sulphonate ring-opened glucose moiety in the ion trap mass spectrometer, m/z 259. An intramolecular transfer mechanism for this fragmentation is presented here for the first time. This fragmentation can be exploited as a general identifier of the glucosinolate class of metabolites in plant extracts and in LCMSn can be employed provide positive identification and quantification of individual glucosinolates. Such approaches offer sensitive tools for focused metabolomics analysis and screening of plant breeding lines.

Phytochemicals for health, the role of pulses.

Pulses are the seeds of legumes that are used for human consumption and include peas, beans, lentils, chickpeas, and fava beans. Pulses are an important source of macronutrients, containing almost twice the amount of protein compared to cereal grains. In addition to being a source of macronutrients and minerals, pulses also contain plant secondary metabolites that are increasingly being recognised for their potential benefits for human health. The best-studied legume is the soybean, traditionally regarded as an oilseed crop rather than a pulse. The potential health benefits of soy, particularly with respect to isoflavone content, have been the subject of much research and the focus of several reviews. By comparison, less is known about pulses. This review investigates the health potential of pulses, examining the bioactivity of pulse isoflavones, phytosterols, resistant starch, bioactive carbohydrates, alkaloids and saponins. The evidence for health properties is considered, as is the effect of processing and cooking on these potentially beneficial phytochemicals.