Zonal Chemical Signal Pathways Mediating Floral Induction in Apple.

Phytohormones that trigger or repress flower meristem development in apple buds are thought to be locally emitted from adjacent plant tissues, including leaves and fruitlets. The presence of fruitlets is known to inhibit adjacent buds from forming flowers and thus fruits. The resulting absence of fruitlets the following season restores flower-promoting signalling to the new buds. The cycle can lead to a biennial bearing behaviour of alternating crop loads in a branch or tree. The hormonal stimuli that elicit flowering 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 cultivar 'Ruby Matilda' were subjected to zonal crop load treatments imposed to two leaders of bi-axis trees in the 2020/2021 season. Buds were collected over the expected FI phase, which is within 60 DAFB. Metabolomics profiling was undertaken to determine the differentially expressed pathways and key signalling molecules associated with FI in the leader and at tree level. Pronounced metabolic differences were observed in trees and leaders with high return bloom with significant increases in compounds belonging to the cytokinin, abscisic acid (ABA), phenylpropanoid and flavanol chemical classes. The presence of cytokinins, namely adenosine, inosine and related derivatives, as well as ABA phytohormones, provides further insight into the chemical intervention opportunities for future crop load management strategies via plant growth regulators.

Development and evaluation of genomics informed real-time PCR assays for the detection and strain typing of Mycobacterium avium subsp. paratuberculosis.

AIMS: This study aimed to identify specific genomic targets for the detection and strain typing of Map and analyse their sensitivity and specificity, and detect Map directly from faeces. METHODS AND RESULTS: A comparative genomics approach was used to identify specific genomic targets for the detection and strain typing of Map. A Map specific qPCR using the primer pair 7132 that targets a DNA segregation ATPase protein was able to detect all strains of Map and is more sensitive than the current Johne's disease PCR assays with a sensitivity of 0.0002 fg µl-1. A strain specific qPCR using the Atsa primer pair that targets the arylsulfase gene was able to differentiate between Type S and Type C strains of Map and was more sensitive than the IS1311 PCR and REA with a sensitivity of 40 fg µl-1 and was specific for Type S Map. Both assays successfully detected Map directly from faeces. CONCLUSION: This study developed and validated two genomics informed qPCR assays, 7132B Map and Atsa Type S and found both assays to be highly specific and sensitive for the detection of Map from culture and directly from faeces. This is the first time that a probe-based qPCR has been designed and developed for Map strain typing, which will greatly improve the response time during outbreak investigations.

A Chiral-LC-MS Method for the Simultaneous Quantification of Short-Chain Fatty Acids and D/L-Lactate in the Ruminal Fluid of Dairy Cows.

Short-chain fatty acids (SCFA) and lactate in ruminal fluid are products resulting from the microbial fermentation of substrates and can be used to reflect the composition and activity of the ruminal microbiome. Determination of SCFA and D-/L-lactate in ruminal fluid currently requires two separate protocols, which is time-consuming and costly. In this study, we have optimised and validated a simple and unified 3-nitrophenylhydrazine (3-NPH) derivatisation protocol and a 20 min chiral-LC-MS method for the simultaneous quantification of all SCFA and D- and L-lactate in ruminal fluid. This method, which requires no sample pretreatment or purification shows adequate sensitivity (limit of detection (LOD): 0.01 µg/mL), satisfactory accuracy (recovery: 88-103%), and excellent reproducibility (relative standard deviation (RSD) for repeated analyses < 3% for most analytes). The application of this method to a cohort of 24 animals allowed us to reveal a large inter-cow variation in ruminal SCFA and lactate level, the concentration range for each species, the widespread correlation between different SCFA, and the strong correlation between D- and L-lactate.

The pan-genome of Mycobacterium avium subsp. paratuberculosis (Map) confirms ancestral lineage and reveals gene rearrangements within Map Type S.

BACKGROUND: To date genomic studies on Map have concentrated on Type C strains with only a few Type S strains included for comparison. In this study the entire pan-genome of 261 Map genomes (205 Type C, 52 Type S and 4 Type B) and 7 Mycobacterium avium complex (Mac) genomes were analysed to identify genomic similarities and differences between the strains and provide more insight into the evolutionary relationship within this Mycobacterial species. RESULTS: Our analysis of the core genome of all the Map isolates identified two distinct lineages, Type S and Type C Map that is consistent with previous phylogenetic studies of Map. Pan-genome analysis revealed that Map has a larger accessory genome than Mycobacterium avium subsp. avium (Maa) and Type C Map has a larger accessory genome than Type S Map. In addition, we found large rearrangements within Type S strains of Map and little to none in Type C and Type B strains. There were 50 core genes identified that were unique to Type S Map and there were no unique core genes identified between Type B and Type C Map strains. In Type C Map we identified an additional CE10 CAZyme class which was identified as an alpha/beta hydrolase and an additional polyketide and non-ribosomal peptide synthetase cluster. Consistent with previous analysis no plasmids and only incomplete prophages were identified in the genomes of Map. There were 45 hypothetical CRISPR elements identified with no associated cas genes. CONCLUSION: This is the most comprehensive comparison of the genomic content of Map isolates to date and included the closing of eight Map genomes. The analysis revealed that there is greater variation in gene synteny within Type S strains when compared to Type C indicating that the Type C Map strain emerged after Type S. Further analysis of Type C and Type B genomes revealed that they are structurally similar with little to no genetic variation and that Type B Map may be a distinct clade within Type C Map and not a different strain type of Map. The evolutionary lineage of Maa and Map was confirmed as emerging after M. hominissuis.

Developing Prediction Models Using Near-Infrared Spectroscopy to Quantify Cannabinoid Content in Cannabis Sativa.

Cannabis is commercially cultivated for both therapeutic and recreational purposes in a growing number of jurisdictions. The main cannabinoids of interest are cannabidiol (CBD) and delta-9 tetrahydrocannabidiol (THC), which have applications in different therapeutic treatments. The rapid, nondestructive determination of cannabinoid levels has been achieved using near-infrared (NIR) spectroscopy coupled to high-quality compound reference data provided by liquid chromatography. However, most of the literature describes prediction models for the decarboxylated cannabinoids, e.g., THC and CBD, rather than naturally occurring analogues, tetrahydrocannabidiolic acid (THCA) and cannabidiolic acid (CBDA). The accurate prediction of these acidic cannabinoids has important implications for quality control for cultivators, manufacturers and regulatory bodies. Using high-quality liquid chromatography-mass spectroscopy (LCMS) data and NIR spectra data, we developed statistical models including principal component analysis (PCA) for data quality control, partial least squares regression (PLS-R) models to predict cannabinoid concentrations for 14 different cannabinoids and partial least squares discriminant analysis (PLS-DA) models to characterise cannabis samples into high-CBDA, high-THCA and even-ratio classes. This analysis employed two spectrometers, a scientific grade benchtop instrument (Bruker MPA II-Multi-Purpose FT-NIR Analyzer) and a handheld instrument (VIAVI MicroNIR Onsite-W). While the models from the benchtop instrument were generally more robust (99.4-100% accuracy prediction), the handheld device also performed well (83.1-100% accuracy prediction) with the added benefits of portability and speed. In addition, two cannabis inflorescence preparation methods were evaluated: finely ground and coarsely ground. The models generated from coarsely ground cannabis provided comparable predictions to that of the finely ground but represent significant timesaving in terms of sample preparation. This study demonstrates that a portable NIR handheld device paired with LCMS quantitative data can provide accurate cannabinoid predictions and potentially be of use for the rapid, high-throughput, nondestructive screening of cannabis material.

Quantitation and Distribution of Epichloë-Derived Alkaloids in Perennial Ryegrass Tissues.

Perennial ryegrass (Lolium perenne L.), an economically important pasture and turf grass, is commonly infected with asexual Epichloë species endophytes. Endophytes provide enhanced bioprotection by producing alkaloids, and research often focusses on the negative impact on grazing animals. However, alkaloid distribution throughout the plant and their role in biocontrol of insect pests and diseases are less well understood. Additionally, intermediate compounds have not been investigated for their impacts on animal welfare and biological control in pasture-based scenarios. Here, a single liquid chromatography-mass spectrometry (LC-MS) method was used to measure seven alkaloids in different perennial ryegrass tissues infected with SE or NEA12 endophytes. High alkaloid recoveries and a clear plant matrix effect emphasize the importance of using matrix-matched standards for accurate quantitation. The method is sensitive, detecting alkaloids at low concentrations (nanogram levels), which is important for endophyte strains that produce compounds detrimental to livestock. Concentrations were generally highest in seeds, but distribution differed in the shoots/roots: peramine, terpendole E, terpendole C and lolitrem B were higher in shoots, whilst ergovaline, paxilline and epoxy-janthitrem I were more evenly distributed throughout the two tissues. Knowledge of alkaloid distribution may allow for concentrations to be predicted in roots based on concentrations in the shoots, thereby assisting future determinations of resistance to insects, especially subterranean root-feeding pests.

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.

The Cannabis Plant as a Complex System: Interrelationships between Cannabinoid Compositions, Morphological, Physiological and Phenological Traits.

Maintaining specific and reproducible cannabinoid compositions (type and quantity) is essential for the production of cannabis-based remedies that are therapeutically effective. The current study investigates factors that determine the plant's cannabinoid profile and examines interrelationships between plant features (growth rate, phenology and biomass), inflorescence morphology (size, shape and distribution) and cannabinoid content. An examination of differences in cannabinoid profile within genotypes revealed that across the cultivation facility, cannabinoids' qualitative traits (ratios between cannabinoid quantities) remain fairly stable, while quantitative traits (the absolute amount of Δ9-tetrahydrocannabinol (THC), cannabidiol (CBD), cannabichromene (CBC), cannabigerol (CBG), Δ9-tetrahydrocannabivarin (THCV) and cannabidivarin (CBDV)) can significantly vary. The calculated broad-sense heritability values imply that cannabinoid composition will have a strong response to selection in comparison to the morphological and phenological traits of the plant and its inflorescences. Moreover, it is proposed that selection in favour of a vigorous growth rate, high-stature plants and wide inflorescences is expected to increase overall cannabinoid production. Finally, a range of physiological and phenological features was utilised for generating a successful model for the prediction of cannabinoid production. The holistic approach presented in the current study provides a better understanding of the interaction between the key features of the cannabis plant and facilitates the production of advanced plant-based medicinal substances.

Production of Primary Metabolites by Rhizopus stolonifer, Causal Agent of Almond Hull Rot Disease.

Species in the fungal genus Rhizopus are able to convert simple sugars into primary metabolites such as fumaric acid, lactic acid, citric acid, and, to a lesser extent, malic acid in the presence of specific carbon and nitrogen sources. This ability has been linked to plant pathogenicity. Rhizopus stolonifer causes hull rot disease in almonds, symptoms of which have been previously associated with the fungus's production of fumaric acid. Six isolates of R. stolonifer taken from infected almond hulls were grown in artificial media amended with one of four carbon sources (glucose, fructose, sucrose, and xylose) and two nitrogen sources (asparagine and ammonium sulphate) chosen based on almond hull composition and used in industry. Proton nuclear magnetic resonance (1H NMR)-based metabolomics identified that R. stolonifer could metabolise glucose, fructose, sucrose, and to a lesser extent xylose, and both nitrogen sources, to produce three metabolites, i.e., fumaric acid, lactic acid, and ethanol, under in vitro conditions. Sugar metabolisation and acid production were significantly influenced by sugar source and isolates, with five isolates depleting glucose most rapidly, followed by fructose, sucrose, and then xylose. The maximum amounts of metabolites were produced when glucose was the carbon source, with fumaric acid produced in higher amounts than lactic acid. Isolate 19A-0069, however, preferred sucrose as the carbon source, and Isolate 19A-0030 produced higher amounts of lactic acid than fumaric acid. This is the first report, to our knowledge, of R. stolonifer producing lactic acid in preference to fumaric acid. Additionally, R. stolonifer isolate 19-0030 was inoculated into Nonpareil almond fruit on trees grown under high- and low-nitrogen and water treatments, and hull compositions of infected and uninfected fruit were analysed using 1H NMR-based metabolomics. Glucose and asparagine content of uninfected hulls was influenced by the nitrogen and water treatments provided to the trees, being higher in the high-nitrogen and water treatments. In infected hulls, glucose and fructose were significantly reduced but not sucrose or xylose. Large amounts of both fumaric and lactic acid were produced, particularly under high-nitrogen treatments. Moreover, almond shoots placed in dilute solutions of fumaric acid or lactic acid developed leaf symptoms very similar to the 'strike' symptoms seen in hull rot disease in the field, suggesting both acids are involved in causing disease.

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.

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.

Dietary wheat and reduced methane yield are linked to rumen microbiome changes in dairy cows.

Fermentation of pasture grasses and grains in the rumen of dairy cows and other ruminants produces methane as a by-product, wasting energy and contributing to the atmospheric load of greenhouse gasses. Many feeding trials in farmed ruminants have tested the impact of dietary components on feed efficiency, productivity and methane yield (MeY). Such diets remodel the rumen microbiome, altering bacterial, archaeal, fungal and protozoan populations, with an altered fermentation outcome. In dairy cows, some dietary grains can reduce enteric methane production. This is especially true of wheat, in comparison to corn or barley. Using a feeding trial of cows fed rolled wheat, corn or barley grain, in combination with hay and canola, we identified wheat-associated changes in the ruminal microbiome. Ruminal methane production, pH and VFA concentration data together with 16S rRNA gene amplicon sequences were used to compare ruminal bacterial and archaeal populations across diets. Differential abundance analysis of clustered sequences (OTU) identified members of the bacterial families Lachnospiraceae, Acidaminococcaceae, Eubacteriaceae, Prevotellaceae, Selenomonadaceae, Anaerovoracaceae and Fibrobacteraceae having a strong preference for growth in wheat-fed cows. Within the methanogenic archaea, (at >99% 16S rRNA sequence identity) the growth of Methanobrevibacter millerae was favoured by the non-wheat diets, while Methanobrevibacter olleyae was unaffected. From the wheat-preferring bacteria, correlation analysis found OTU strongly linked to reduced MeY, reduced pH and raised propionic acid levels. OTU from the genera Shuttleworthia and Prevotella_7 and especially Selenomonadaceae had high anti-methane correlations. An OTU likely representing (100% sequence identity) the fumarate-reducing, hydrogen-utilising, rumen bacterium Mitsuokella jalaludinii, had an especially high negative correlation coefficient (-0.83) versus MeY and moderate correlation (-0.6) with rumen pH, strongly suggesting much of the MeY suppression is due to reduced hydrogen availablity. Other OTU, representing as yet unknown species from the Selenomonadaceae family and the genera Prevotella_7, Fibrobacter and Syntrophococcus also had high to moderate negative MeY correlations, but low correlation with pH. These latter likely represent bacterial species able to reduce MeY without causing greater ruminal acidity, making them excellent candidates, provided they can be isolated, for development as anti-methane probiotics.

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.

High-Throughput Quantitation of Cannabinoids by Liquid Chromatography Triple-Quadrupole Mass Spectrometry.

The high-throughput quantitation of cannabinoids is important for the cannabis industry. As medicinal products increase, and research into compounds that have pharmacological benefits increase, and the need to quantitate more than just the main cannabinoids becomes more important. This study aims to provide a rapid, high-throughput method for cannabinoid quantitation using a liquid chromatography triple-quadrupole mass spectrometer (LC-QQQ-MS) with an ultraviolet diode array detector (UV-DAD) for 16 cannabinoids: CBDVA, CBDV, CBDA, CBGA, CBG, CBD, THCV, THCVA, CBN, CBNA, THC, Δ8-THC, CBL, CBC, THCA-A and CBCA. Linearity, limit of detection (LOD), limit of quantitation (LOQ), accuracy, precision, recovery and matrix effect were all evaluated. The validated method was used to determine the cannabinoid concentration of four different Cannabis sativa strains and a low THC strain, all of which have different cannabinoid profiles. All cannabinoids eluted within five minutes with a total analysis time of eight minutes, including column re-equilibration. This was twice as fast as published LC-QQQ-MS methods mentioned in the literature, whilst also covering a wide range of cannabinoid compounds.

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).

Optimised Method for Short-Chain Fatty Acid Profiling of Bovine Milk and Serum.

Short-chain fatty acids (SCFA, C2-C5) in milk and serum are derived from rumen bacterial fermentation and, thus, have the potential to be used as biomarkers for the health status of dairy cows. Currently, there is no comprehensive and validated method that can be used to analyse all SCFAs in both bovine serum and milk. This paper reports an optimised protocol, combining 3-nitrophenylhydrazine (3-NPH) derivatisation and liquid chromatography-mass spectrometry (LC-MS) analysis for quantification of SCFA and ß-hydroxybutyric acid (BHBA) in both bovine milk and bovine serum. This method is sensitive (limit of detection (LOD) ≤ 0.1 µmol/L of bovine milk and serum), accurate (recovery 84-115% for most analytes) and reproducible (relative standard deviation (RSD) for repeated analyses below 7% for most measurements) with a short sample preparation step. The application of this method to samples collected from a small cohort of animals allowed us to reveal a large variation in SCFA concentration between serum and milk and across different animals as well as the strong correlation of some SCFAs between milk and serum samples.

A Metabolomic Study of Epichloë Endophytes for Screening Antifungal Metabolites.

Epichloë endophytes, fungal endosymbionts of Pooidae grasses, are commonly utilized in forage and turf industries because they produce beneficial metabolites that enhance resistance against environmental stressors such as insect feeding and disease caused by phytopathogen infection. In pastoral agriculture, phytopathogenic diseases impact both pasture quality and animal production. Recently, bioactive endophyte strains have been reported to secrete compounds that significantly inhibit the growth of phytopathogenic fungi in vitro. A screen of previously described Epichloë-produced antifeedant and toxic alkaloids determined that the antifungal bioactivity observed is not due to the production of these known metabolites, and so there is a need for methods to identify new bioactive metabolites. The process described here is applicable more generally for the identification of antifungals in new endophytes. This study aims to characterize the fungicidal potential of novel, 'animal friendly' Epichloë endophyte strains NEA12 and NEA23 that exhibit strong antifungal activity using an in vitro assay. Bioassay-guided fractionation, followed by metabolite analysis, identified 61 metabolites that, either singly or in combination, are responsible for the observed bioactivity. Analysis of the perennial ryegrass-endophyte symbiota confirmed that NEA12 and NEA23 produce the prospective antifungal metabolites in symbiotic association and thus are candidates for compounds that promote disease resistance in planta. The "known unknown" suite of antifungal metabolites identified in this study are potential biomarkers for the selection of strains that enhance pasture and turf production through better disease control.

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.

Regio-distribution and double bond locations of unsaturated fatty acids in phospholipids of bovine milk.

Hundreds of phospholipid (PL) species with defined fatty acid (FA) composition have been identified previously in bovine milk using liquid chromatography tandem mass spectrometry (LC-MS/MS). Paterno-Buchi photochemical reaction coupled with LC-MS/MS was applied in this study to further unravel the regio-distribution and double bond (DB) locations of FAs. Using SPE-purified PLs and 2-acetylpyridine as the photochemical derivatization reagent, we were able to reveal the non-specific regio-distribution of unsaturated FAs and the widespread occurrence of regioisomers in milk PLs. Although Δ9 and Δ9,12 were found to be the predominant DB location(s) for C18:1 and C18:2 respectively, other DB positional isomers such as C18:1Δ11, C18:1Δ12 and C18:1Δ13 and C18:2Δ9,11 were widely detected in PL structures, implying that the minor isomers of C18:1 and C18:2 equally participate in the synthesis of PLs. Our study provides novel information on the fine structure of milk PLs and further underlines the complexity of milk lipid composition.

Comparison of Workflows for Milk Lipid Analysis: Phospholipids.

Milk is a rich source of lipids, with the major components being triglycerides (TAG) and phospholipids (mainly phosphatidylcholine (PC), sphingomyelin (SM), phosphatidylethanolamine (PE), phosphatidylserine (PS) and phosphatidylinositol (PI)). Liquid chromatography-mass spectrometry (LC-MS) is the predominant technique for lipid identification and quantification across all biological samples. While fatty acid (FA) composition of the major lipid classes of milk can be readily determined using tandem MS, elucidating the regio-distribution and double bond position of the FA remains difficult. Various workflows have been reported on the quantification of lipid species in biological samples in the past 20 years, but no standard or consensus methods are currently available for the quantification of milk phospholipids. This study will examine the influence of several common factors in lipid analysis workflow (including lipid extraction protocols, LC stationary phases, mobile phase buffers, gradient elution programmes, mass analyser resolution and isotope correction) on the quantification outcome of bovine milk phospholipids. The pros and cons of the current LC-MS methods as well as the critical problems to be solved will also be discussed.