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.

Metabolic Pathways for Observed Impacts of Crop Load on Floral Induction in Apple

The triggers of biennial bearing are thought to coincide with embryonic development in apple and occurs within the first 70 days after full bloom (DAFB). Strong evidence suggests hormonal signals are perceived by vegetative apple spur buds to induce flowering. The hormonal response is typically referred to as the floral induction (FI) phase in bud meristem development. To determine the metabolic pathways activated in FI, young trees of the biennial bearing cultivar 'Nicoter' and the less susceptible cultivar 'Rosy Glow' were forced into an alternate cropping cycle over five years and an inverse relationship of crop load and return bloom was established. Buds were collected over a four-week duration within 70 DAFB from trees that had maintained a four-year biennial bearing cycle. Metabolomics profiling was undertaken to determine the differentially expressed pathways and key signalling molecules associated with biennial bearing. Marked metabolic differences were observed in trees with high and low crop load treatments. Significant effects were detected in members of the phenylpropanoid pathway comprising hydroxycinnamates, salicylates, salicylic acid biosynthetic pathway intermediates and flavanols. This study identifies plant hormones associated with FI in apples using functional metabolomics analysis.

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).

Utilisation of Design of Experiments Approach to Optimise Supercritical Fluid Extraction of Medicinal Cannabis

Carbon dioxide supercritical fluid extraction (CO2 SFE) is a clean and cost-effective method of extracting cannabinoids from cannabis. Using design of experiment methodologies an optimised protocol for extraction of medicinal cannabis bud material (population of mixed plants, combined THC:CBD approximately 1:1.5) was developed at a scale of one kg per extraction. Key variables investigated were CO2 flow rate, extraction time and extraction pressure. A total of 15 batches were analysed for process development using a two-level, full factorial design of experiments for three variable factors over eleven batches. The initial eleven batches demonstrated that CO2 flow rate has the most influence on the overall yield and recovery of the key cannabinoids, particularly CBD. The additional four batches were conducted as replicated runs at high flow rates to determine reproducibility. The highest extraction weight of 71 g (7.1%) was obtained under high flow rate (150 g/min), with long extraction time (600 min) at high pressure (320 bar). This method also gave the best recoveries of THC and CBD. This is the first study to report the repeated extraction of large amounts of cannabis (total 15 kg) to optimise the CO2 SFE extraction process for a pharmaceutical product.

A Multiple Protease Strategy to Optimise the Shotgun Proteomics of Mature Medicinal Cannabis Buds

Earlier this year we published a method article aimed at optimising protein extraction from mature buds of medicinal cannabis for trypsin-based shotgun proteomics (Vincent, D., et al. Molecules2019, 24, 659). We then developed a top-down proteomics (TDP) method (Vincent, D., et al. Proteomes2019, 7, 33). This follow-up study aims at optimising the digestion of medicinal cannabis proteins for identification purposes by bottom-up and middle-down proteomics (BUP and MDP). Four proteases, namely a mixture of trypsin/LysC, GluC, and chymotrypsin, which target different amino acids (AAs) and therefore are orthogonal and cleave proteins more or less frequently, were tested both on their own as well as sequentially or pooled, followed by nLC-MS/MS analyses of the peptide digests. Bovine serum albumin (BSA, 66 kDa) was used as a control of digestion efficiency. With this multiple protease strategy, BSA was reproducibly 97% sequenced, with peptides ranging from 0.7 to 6.4 kD containing 5 to 54 AA residues with 0 to 6 miscleavages. The proteome of mature apical buds from medicinal cannabis was explored more in depth with the identification of 27,123 peptides matching 494 unique accessions corresponding to 229 unique proteins from Cannabis sativa and close relatives, including 130 (57%) additional annotations when the list is compared to that of our previous BUP study (Vincent, D., et al. Molecules2019, 24, 659). Almost half of the medicinal cannabis proteins were identified with 100% sequence coverage, with peptides composed of 7 to 91 AA residues with up to 9 miscleavages and ranging from 0.6 to 10 kDa, thus falling into the MDP domain. Many post-translational modifications (PTMs) were identified, such as oxidation, phosphorylations, and N-terminus acetylations. This method will pave the way for deeper proteome exploration of the reproductive organs of medicinal cannabis, and therefore for molecular phenotyping within breeding programs.

Metabolomics approaches for the discrimination of disease suppressive soils for Rhizoctonia solani AG8 in cereal crops using 1H NMR and LC-MS

The suppression of soilborne crop pathogens such as Rhizoctonia solani AG8 may offer a sustainable and enduring method for disease control, though soils with these properties are difficult to identify. In this study, we analysed the soil metabolic profiles of suppressive and non-suppressive soils over 2 years of cereal production. We collected bulk and rhizosphere soil at different cropping stages and subjected soil extracts to liquid chromatography-mass spectrometry (LC-MS) and proton nuclear magnetic resonance spectroscopy (¹H NMR) analyses. Community analyses of suppressive and non-suppressive soils using principal component analyses and predictive modelling of LC-MS and NMR datasets respectively, revealed distinct biochemical profiles for the two soil types with clustering based on suppressiveness and cropping stage. NMR spectra revealed the suppressive soils to be more abundant in sugar molecules than non-suppressive soils, which were more abundant in lipids and terpenes. LC-MS features that were significantly more abundant in the suppressive soil were identified and assessed as potential biomarkers for disease suppression. The structures of a potential class of LC-MS biomarkers were elucidated using accurate mass data and MS fragmentation spectrum information. The most abundant compound found in association with suppressive soils was confirmed to be a macrocarpal, which is an antimicrobial secondary metabolite. Our study has demonstrated the utility of environmental metabolomics for the study of disease suppressive soils, resulting in the discovery of a macrocarpal biomarker for R. solani AG8 suppressive soil which can be further studied functionally in association with suppression pot trials and microbial isolation studies.

Comparison of methylation methods for fatty acid analysis of milk fat

Three acid- and alkaline-catalysed transesterification methods were compared with the aim to validate a simple yet reliable protocol for fatty acid (FA) profiling of milk fat. While both the acid- and alkaline-catalysed methods were able to convert completely triglycerides and phospholipids into fatty acid methyl esters (FAMEs), the acid catalyst caused significant degradation of conjugated linoleic acid C18:2c9t11 at high temperature. Although a milder temperature can mitigate this negative impact, a long reaction time (2 h) is required to achieve full methylation. By contrast, despite being unable to methylate free fatty acids (FFA), the alkaline-catalysed transesterification yielded comparable results for all major FA due to the very low level of FFA in milk. The alkaline-catalysed methylation is benign for C18:2c9t11. We recommend here a simple one-step protocol based on 0.2 M methanolic KOH, a short reaction time (20 min) and a mild reaction temperature (50 °C) for milk FAME preparation.

Quantitation and Identification of Intact Major Milk Proteins for High-Throughput LC-ESI-Q-TOF MS Analyses

Cow's milk is an important source of proteins in human nutrition. On average, cow's milk contains 3.5% protein. The most abundant proteins in bovine milk are caseins and some of the whey proteins, namely beta-lactoglobulin, alpha-lactalbumin, and serum albumin. A number of allelic variants and post-translationally modified forms of these proteins have been identified. Their occurrence varies with breed, individuality, stage of lactation, and health and nutritional status of the animal. It is therefore essential to have reliable methods of detection and quantitation of these proteins. Traditionally, major milk proteins are quantified using liquid chromatography (LC) and ultra violet detection method. However, as these protein variants co-elute to some degree, another dimension of separation is beneficial to accurately measure their amounts. Mass spectrometry (MS) offers such a tool. In this study, we tested several RP-HPLC and MS parameters to optimise the analysis of intact bovine proteins from milk. From our tests, we developed an optimum method that includes a 20-28-40% phase B gradient with 0.02% TFA in both mobile phases, at 0.2 mL/min flow rate, using 75°C for the C8 column temperature, scanning every 3 sec over a 600-3000 m/z window. The optimisations were performed using external standards commercially purchased for which ionisation efficiency, linearity of calibration, LOD, LOQ, sensitivity, selectivity, precision, reproducibility, and mass accuracy were demonstrated. From the MS analysis, we can use extracted ion chromatograms (EICs) of specific ion series of known proteins and integrate peaks at defined retention time (RT) window for quantitation purposes. This optimum quantitative method was successfully applied to two bulk milk samples from different breeds, Holstein-Friesian and Jersey, to assess differences in protein variant levels.

Milk Bottom-Up Proteomics: Method Optimization

Milk is a complex fluid whose proteome displays a diverse set of proteins of high abundance such as caseins and medium to low abundance whey proteins such as ß-lactoglobulin, lactoferrin, immunoglobulins, glycoproteins, peptide hormones, and enzymes. A sample preparation method that enables high reproducibility and throughput is key in reliably identifying proteins present or proteins responding to conditions such as a diet, health or genetics. Using skim milk samples from Jersey and Holstein-Friesian cows, we compared three extraction procedures which have not previously been applied to samples of cows' milk. Method A (urea) involved a simple dilution of the milk in a urea-based buffer, method B (TCA/acetone) involved a trichloroacetic acid (TCA)/acetone precipitation, and method C (methanol/chloroform) involved a tri-phasic partition method in chloroform/methanol solution. Protein assays, SDS-PAGE profiling, and trypsin digestion followed by nanoHPLC-electrospray ionization-tandem mass spectrometry (nLC-ESI-MS/MS) analyses were performed to assess their efficiency. Replicates were used at each analytical step (extraction, digestion, injection) to assess reproducibility. Mass spectrometry (MS) data are available via ProteomeXchange with identifier PXD002529. Overall 186 unique accessions, major and minor proteins, were identified with a combination of methods. Method C (methanol/chloroform) yielded the best resolved SDS-patterns and highest protein recovery rates, method A (urea) yielded the greatest number of accessions, and, of the three procedures, method B (TCA/acetone) was the least compatible of all with a wide range of downstream analytical procedures. Our results also highlighted breed differences between the proteins in milk of Jersey and Holstein-Friesian cows.

NMR metabolomics for soil analysis provide complementary, orthogonal data to MIR and traditional soil chemistry approaches--a land use study

The present study was designed to analyse soils by different methodologies to determine the range of traits that could be investigated for the study of environmental soil samples. Proton nuclear magnetic resonance spectroscopy ((1) H NMR) was employed for metametabolomic analysis of soils from agricultural systems (managed) or from soils in a native state (remnant). The metabolomic methodologies employed (grinding and extraction with sonication) are capable of breaking up cell walls and so enabled characterisation of both extracellular and intracellular components of soil. Diffuse mid-infrared spectroscopy (MIR) data was obtained for the same sample sets, and in addition, elemental composition was determined by conventional laboratory chemical testing methods. Also investigated was the antibiotic activity of the soil extracts. Resilient or suppressive soils are valued in the agricultural setting as they convey disease resistance (against bacterial and fungal pathogens) to crop plants. In order to test if any such biological activity could be detected in the soils, the extracts were tested against the bacteria Bacillus subtilis. Several extracts showed strong growth inhibition against the bacteria with the most active clustered together in principle component analysis (PCA) of the metabolomic data. The study showed that the NMR metabolomic approach corresponds more accurately to land use and biochemical properties potentially associated with suppression, while MIR data correlated well to inorganic chemical analysis. Thus, the study demonstrates the utility in combining these spectroscopic methods for soil analysis.

Pulses for Human Health: Changes in Isoflavone and Saponin Content with Preparation and Cooking

Pulses are the seeds of legumes that are used for human consumption and include peas, beans, lentils, chickpeas, and faba beans. The bioactivity of pulse metabolites, including isoflavones and saponins, has been the subject of considerable research. However, there has been less consideration regarding the effect of cooking on these potentially beneficial phytochemicals. In this study the changes in concentration of isoflavones and saponins in 13 varieties of pulse including field pea, chickpea, and lentil is studied in whole seed, hydrated seed, and cooked seed. It was found that the concentration of isoflavones studied (genistein, daidzein, formononetin, and biochanin A) was highest in chickpeas, that soaking altered the amount of isoflavones, and that cooking eliminated these isoflavones. By contrast the saponin content of the pulses was more varied and less effect of cooking was observed. This has implications for any dietary recommendation for these pulse varieties with respect to these micro-nutrients.

International NMR-based environmental metabolomics intercomparison exercise

Several fundamental requirements must be met so that NMR-based metabolomics and the related technique of metabonomics can be formally adopted into environmental monitoring and chemical risk assessment. Here we report an intercomparison exercise which has evaluated the effectiveness of 1H NMR metabolomics to generate comparable data sets from environmentally derived samples. It focuses on laboratory practice that follows sample collection and metabolite extraction, specifically the final stages of sample preparation, NMR data collection (500, 600, and 800 MHz), data processing, and multivariate analysis. Seven laboratories have participated from the U.S.A., Canada, U.K., and Australia, generating a total of ten data sets. Phase 1 comprised the analysis of synthetic metabolite mixtures, while Phase 2 investigated European flounder (Platichthys flesus) liver extracts from clean and contaminated sites. Overall, the comparability of data sets from the participating laboratories was good. Principal components analyses (PCA) of the individual data sets yielded ten highly similar scores plots for the synthetic mixtures, with a comparable result for the liver extracts. Furthermore, the same metabolic biomarkers that discriminated fish from clean and contaminated sites were discovered by all the laboratories. PCA of the combined data sets showed excellent clustering of the multiple analyses. These results demonstrate that NMR-based metabolomics can generate data that are sufficiently comparable between laboratories to support its continued evaluation for regulatory environmental studies.