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Cannabis is cultivated for therapeutic and recreational purposes where delta-9 tetrahydrocannabinol (THC) is a main target for its therapeutic effects. As the global cannabis industry and research into cannabinoids expands, more efficient and cost-effective analysis methods for determining cannabinoid concentrations will be beneficial to increase efficiencies and maximize productivity. The utilization of machine learning tools to develop near-infrared (NIR) spectroscopy-based prediction models, which have been validated from accurate and sensitive chemical analysis, such as gas chromatography (GC) or liquid chromatography mass spectroscopy (LCMS), is essential. Previous research on cannabinoid prediction models targeted decarboxylated cannabinoids, such as THC, rather than the naturally occurring precursor, tetrahydrocannabinolic acid (THCA), and utilize finely ground cannabis inflorescence. The current study focuses on building prediction models for THCA concentrations in whole cannabis inflorescences prior to harvest, by employing non-destructive screening techniques so cultivators may rapidly characterize high-performing cultivars for chemotype in real time, thus facilitating targeted optimization of crossbreeding efforts. Using NIR spectroscopy and LCMS to create prediction models we can differentiate between high-THCA and even ratio classes with 100% prediction accuracy. We have also developed prediction models for THCA concentration with a R2 = 0.78 with a prediction error average of 13%. This study demonstrates the viability of a portable handheld NIR device to predict THCA concentrations on whole cannabis samples before harvest, allowing the evaluation of cannabinoid profiles to be made earlier, therefore increasing high-throughput and rapid capabilities. [ABSTRACT FROM AUTHOR]

Abstract: Symbiotic associations between tall fescue grasses and asexual Epichloe fungal endophytes exhibit biosynthesis of alkaloid compounds causing both beneficial and detrimental effects. Candidate novel endophytes with favourable chemotypic profiles [...]

Many genome variants shaping mammalian phenotype are hypothesized to regulate gene transcription and/or to be under selection. However, most of the evidence to support this hypothesis comes from human studies. Systematic evidence for regulatory and evolutionary signals contributing to complex traits in a different mammalian model is needed. Sequence variants associated with gene expression (expression quantitative trait loci [eQTLs]) and concentration of metabolites (metabolic quantitative trait loci [mQTLs]) and under histone-modification marks in several tissues were discovered from multiomics data of over 400 cattle. Variants under selection and evolutionary constraint were identified using genome databases of multiple species. These analyses defined 30 sets of variants, and for each set, we estimated the genetic variance the set explained across 34 complex traits in 11,923 bulls and 32,347 cows with 17,669,372 imputed variants. The per-variant trait heritability of these sets across traits was highly consistent (r > 0.94) between bulls and cows. Based on the per-variant heritability, conserved sites across 100 vertebrate species and mQTLs ranked the highest, followed by eQTLs, young variants, those under histone-modification marks, and selection signatures. From these results,we defined a Functional-And-Evolutionary Trait Heritability (FAETH) score indicating the functionality and predicted heritability of each variant. In additional 7,551 cattle, the high FAETH-ranking variants had significantly increased genetic variances and genomic prediction accuracies in 3 production traits compared to the low FAETH-ranking variants. The FAETH framework combines the information of gene regulation, evolution, and trait heritability to rank variants, and the publicly available FAETH data provide a set of biological priors for cattle genomic selection worldwide. [ABSTRACT FROM AUTHOR]

Atmospheric CO2 concentrations are predicted to double by the end of this century. Although the effects of CO2 fertilisation in crop systems have been well studied, little is known about the specific interactions among plants, pests and pathogens under a changing climate. This growth chamber study focuses on the interactions among Barley yellow dwarf virus (BYDV), its aphid vector (Rhopalosiphum padi) and wheat (Triticum aestivum L. cv. Yitpi) under ambient (aCO2; 400 μmol mol-1) or elevated (eCO2; 650 μmol mol-1) CO2 concentrations. eCO2 increased the tiller number and biomass of uninoculated plants and advanced the yellowing symptoms of infected plants. Total foliar C content (percentage of the total DW) increased with eCO2 and with sham inoculation (exposed to early herbivory), whereas total N content decreased with eCO2. Liquid chromatography-mass spectrometry approaches were used to quantify the products of primary plant metabolism. eCO2 significantly increased sugars (fructose, mannitol and trehalose), irrespective of disease status, whereas virus infection significantly increased the amino acids essential to aphid diet (histidine, lysine, phenylalanine and tryptophan) irrespective of CO2 concentration. Citric acid was reduced by both eCO2 and virus infection. Both the potential positive and negative biochemical impacts on wheat, aphid and BYDV interactions are discussed. [ABSTRACT FROM AUTHOR]

Asexual species of the genus Epichloë (Clavicipitaceae, Ascomycota) form endosymbiotic associations with Pooidae grasses. This association is important both ecologically and to the pasture and turf industries, as the endophytic fungi confer a multitude of benefits to their host plant that improve competitive ability and performance such as growth promotion, abiotic stress tolerance, pest deterrence and increased host disease resistance. Biotic stress tolerance conferred by the production of bioprotective metabolites has a critical role in an industry context. While the known antimammalian and insecticidal toxins are well characterized due to their impact on livestock welfare, antimicrobial metabolites are less studied. Both pasture and turf grasses are challenged by many phytopathogenic diseases that result in significant economic losses and impact livestock health. Further investigations of Epichloë endophytes as natural biocontrol agents can be conducted on strains that are safe for animals. With the additional benefits of possessing host disease resistance, these strains would increase their commercial importance. Field reports have indicated that pasture grasses associated with Epichloë endophytes are superior in resisting fungal pathogens. However, only a few antifungal compounds have been identified and chemically characterized, and these from sexual (pathogenic) Epichloë species, rather than those utilized to enhance performance in turf and pasture industries. This review provides insight into the various strategies reported in identifying antifungal activity from Epichloë endophytes and, where described, the associated antifungal metabolites responsible for the activity. [ABSTRACT FROM AUTHOR]

Abstract: 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 LCMS n 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. [Copyright &y& Elsevier]