Integrating biophysical crop growth models and whole genome prediction for their mutual benefit: a case study in wheat phenology.

Running crop growth models (CGM) coupled with whole genome prediction (WGP) as a CGM-WGP model introduces environmental information to WGP and genomic relatedness information to the genotype-specific parameters modelled through CGMs. Previous studies have primarily used CGM-WGP to infer prediction accuracy without exploring its potential to enhance CGM and WGP. Here, we implemented a heading and maturity date wheat phenology model within a CGM-WGP framework and compared it with CGM and WGP. The CGM-WGP resulted in more heritable genotype-specific parameters with more biologically realistic correlation structures between genotype-specific parameters and phenology traits compared with CGM-modelled genotype-specific parameters that reflected the correlation of measured phenotypes. Another advantage of CGM-WGP is the ability to infer accurate prediction with much smaller and less diverse reference data compared with that required for CGM. A genome-wide association analysis linked the genotype-specific parameters from the CGM-WGP model to nine significant phenology loci including Vrn-A1 and the three PPD1 genes, which were not detected for CGM-modelled genotype-specific parameters. Selection on genotype-specific parameters could be simpler than on observed phenotypes. For example, thermal time traits are theoretically more independent candidates, compared with the highly correlated heading and maturity dates, which could be used to achieve an environment-specific optimal flowering period. CGM-WGP combines the advantages of CGM and WGP to predict more accurate phenotypes for new genotypes under alternative or future environmental conditions.

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.

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.

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.

Within and combined season prediction models for perennial ryegrass biomass yield using ground- and air-based sensor data.

Across-season biomass assessment is crucial in the cultivar selection process to accurately evaluate the yield performance of lines under different growing conditions. However, it has been difficult to have an accurate, reliable, and repeated fresh biomass (FM) estimation of large populations of plants in the field without destructive harvesting, which incurs significant labor and operation costs. Sensor-based phenotyping platforms have advanced in the data collection of structural and vegetative information of plants, but the developed prediction models are still limited by low correlations at different growth stages and seasons. In this study, our objective was to develop and validate the global prediction models for across-season harvested fresh biomass (FM) yield based on the ground- and air-based sensor data including ground-based LiDAR, ground-based ultrasonic, and air-based multispectral camera to extract LiDAR plant volume (LV), LiDAR point density (LV_Den), height, and Normalized Difference Vegetative Index (NDVI). The study was conducted in a row-plot field trial with 480 rows (3 rows in a plot per cultivar) throughout the whole 2020 growing season up to the reproductive stage. We evaluated the performance of each plant parameter, their relationship, and the best subset prediction models using statistical stepwise selection at the row and plot levels through the seasonal and combined seasonal datasets. The best performing model: F M ~ L V ∗ L V _ D e n ∗ N D V I had a determination of coefficient R 2 of at least 0.9 in vegetative stages and 0.8 in the reproductive stage. Similar results can be achieved in a simpler model with just two LiDAR variables- F M ~ L V ∗ L V _ D e n . In addition, LV and LV_Den showed a robust correlation with FM on their own over seasons and growth stages, while NDVI only performed well in some seasons. The simpler model based on only LiDAR data can be widely applied over season without the need of additional sensor data and may thus make the in-field across-season biomass assessment more feasible and practical for fast and cost-effective development of higher biomass yield cultivars.

Empirical Evaluation of Inflorescences' Morphological Attributes for Yield Optimization of Medicinal Cannabis Cultivars.

In recent decades with the reacknowledgment of the medicinal properties of Cannabis sativa L. (cannabis) plants, there is an increased demand for high performing cultivars that can deliver quality products for various applications. However, scientific knowledge that can facilitate the generation of advanced cannabis cultivars is scarce. In order to improve cannabis breeding and optimize cultivation techniques, the current study aimed to examine the morphological attributes of cannabis inflorescences using novel image analysis practices. The investigated plant population comprises 478 plants ascribed to 119 genotypes of high-THC or blended THC-CBD ratio that was cultivated under a controlled environment facility. Following harvest, all plants were manually processed and an image of the trimmed and refined inflorescences extracted from each plant was captured. Image analysis was then performed using in-house custom-made software which extracted 8 morphological features (such as size, shape and perimeter) for each of the 127,000 extracted inflorescences. Our findings suggest that environmental factors play an important role in the determination of inflorescences' morphology. Therefore, further studies that focus on genotype X environment interactions are required in order to generate inflorescences with desired characteristics. An examination of the intra-plant inflorescences weight distribution revealed that processing 75% of the plant's largest inflorescences will gain 90% of its overall yield weight. Therefore, for the optimization of post-harvest tasks, it is suggested to evaluate if the benefits from extracting and processing the plant's smaller inflorescences outweigh its operational costs. To advance selection efficacy for breeding purposes, a prediction equation for forecasting the plant's production biomass through width measurements of specific inflorescences, formed under the current experimental methodology, was generated. Thus, it is anticipated that findings from the current study will contribute to the field of medicinal cannabis by improving targeted breeding programs, advancing crop productivity and enhancing the efficacy of post-harvest procedures.

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.

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.

The Known Antimammalian and Insecticidal Alkaloids Are Not Responsible for the Antifungal Activity of Epichloë Endophytes.

Asexual Epichloë sp. endophytes in association with pasture grasses produce agronomically important alkaloids (e.g., lolitrem B, epoxy-janthitrems, ergovaline, peramine, and lolines) that exhibit toxicity to grazing mammals and/or insect pests. Novel strains are primarily characterised for the presence of these compounds to ensure they are beneficial in an agronomical setting. Previous work identified endophyte strains that exhibit enhanced antifungal activity, which have the potential to improve pasture and turf quality as well as animal welfare through phytopathogen disease control. The contribution of endophyte-derived alkaloids to improving pasture and turf grass disease resistance has not been closely examined. To assess antifungal bioactivity, nine Epichloë related compounds, namely peramine hemisulfate, n-formylloline-d3, n-acetylloline hydrochloride, lolitrem B, janthitrem A, paxilline, terpendole E, terpendole C, and ergovaline, and four Claviceps purpurea ergot alkaloids, namely ergotamine, ergocornine, ergocryptine, and ergotaminine, were tested at concentrations higher than observed in planta in glasshouse and field settings using in vitro agar well diffusion assays against three common pasture and turf phytopathogens, namely Ceratobasidium sp., Drechslera sp., and Fusarium sp. Visual characterisation of bioactivity using pathogen growth area, mycelial density, and direction of growth indicated no inhibition of pathogen growth. This was confirmed by statistical analysis. The compounds responsible for antifungal bioactivity of Epichloë endophytes hence remain unknown and require further investigation.

Effects of ergotamine on the central nervous system using untargeted metabolomics analysis in a mouse model.

The ergot alkaloid ergotamine is produced by Claviceps purpurea, a parasitic fungus that commonly infects crops and pastures of high agricultural and economic importance. In humans and livestock, symptoms of ergotism include necrosis and gangrene, high blood pressure, heart rate, thermoregulatory dysfunction and hallucinations. However, ergotamine is also used in pharmaceutical applications to treat migraines and stop post-partum hemorrhage. To define its effects, metabolomic profiling of the brain was undertaken to determine pathways perturbed by ergotamine treatment. Metabolomic profiling identified the brainstem and cerebral cortex as regions with greatest variation. In the brainstem, dysregulation of the neurotransmitter epinephrine, and the psychoactive compound 2-arachidonylglycerol was identified. In the cerebral cortex, energy related metabolites isobutyryl-L-carnitine and S-3-oxodecanoyl cysteamine were affected and concentrations of adenylosuccinate, a metabolite associated with mental retardation, were higher. This study demonstrates, for the first time, key metabolomic pathways involved in the behavioural and physiological dysfunction of ergot alkaloid intoxicated animals.

Fungus-originated glucanase and monooxygenase genes in creeping bent grass (Agrostis stolonifera L.).

Recent studies have revealed presence of fungus-originated genes in genomes of cool-season grasses, suggesting occurrence of multiple ancestral gene transfer events between the two distant lineages. The current article describes identification of glucanase-like and monooxygenase-like genes from creeping bent grass, as lateral gene transfer candidates. An in silico analysis suggested presence of the glucanase-like gene in Agrostis, Deyeuxia, and Polypogon genera, but not in other species belonging to the clade 1 of the Poeae tribe. Similarly, the monooxygenase-like gene was confined to Agrostis and Deyeuxia genera. A consistent result was obtained from PCR-based screening. The glucanase-like gene was revealed to be ubiquitously expressed in young seedlings of creeping bent grass. Although expression of the monooxygenase-like gene was suggested in plant tissues, the levels were considerably lower than those of the glucanase-like gene. A phylogenetic analysis revealed close relationships of the two genes between the corresponding genes in fungal endophyte species of the Epichloë genus, suggesting that the genes originated from the Epichloë lineage.

Global Changes in Asexual Epichloë Transcriptomes during the Early Stages, from Seed to Seedling, of Symbiotum Establishment.

Asexual Epichloë fungi are strictly seed-transmitted endophytic symbionts of cool-season grasses and spend their entire life cycle within the host plant. Endophyte infection can confer protective benefits to its host through the production of bioprotective compounds. Inversely, plants provide nourishment and shelter to the resident endophyte in return. Current understanding of the changes in global gene expression of asexual Epichloë endophytes during the early stages of host-endophyte symbiotum is limited. A time-course study using a deep RNA-sequencing approach was performed at six stages of germination, using seeds infected with one of three endophyte strains belonging to different representative taxa. Analysis of the most abundantly expressed endophyte genes identified that most were predicted to have a role in stress and defence responses. The number of differentially expressed genes observed at early time points was greater than those detected at later time points, suggesting an active transcriptional reprogramming of endophytes at the onset of seed germination. Gene ontology enrichment analysis revealed dynamic changes in global gene expression consistent with the developmental processes of symbiotic relationships. Expression of pathway genes for biosynthesis of key secondary metabolites was studied comprehensively and fuzzy clustering identified some unique expression patterns. Furthermore, comparisons of the transcriptomes from three endophyte strains in planta identified genes unique to each strain, including genes predicted to be associated with secondary metabolism. Findings from this study highlight the importance of better understanding the unique properties of individual endophyte strains and will serve as an excellent resource for future studies of host-endophyte interactions.

The characterization of key physiological traits of medicinal cannabis (Cannabis sativa L.) as a tool for precision breeding.

BACKGROUND: For millennia, drug-type cannabis strains were extensively used for various medicinal, ritual, and inebriant applications. However, cannabis prohibition during the last century led to cultivation and breeding activities being conducted under clandestine conditions, while scientific development of the crop ceased. Recently, the potential of medicinal cannabis has been reacknowledged and the now expanding industry requires optimal and scientifically characterized varieties. However, scientific knowledge that can propel this advancement is sorely lacking. To address this issue, the current study aims to provide a better understanding of key physiological and phenological traits that can facilitate the breeding of advanced cultivars. RESULTS: A diverse population of 121 genotypes of high-THC or balanced THC-CBD ratio was cultivated under a controlled environment facility and 13 plant parameters were measured. No physiological association across genotypes attributed to the same vernacular classification was observed. Floral bud dry weight was found to be positively associated with plant height and stem diameter but not with days to maturation. Furthermore, the heritability of both plant height and days to maturation was relatively high, but for plant height it decreased during the vegetative growth phase. To advance breeding efficacy, a prediction equation for forecasting floral bud dry weight was generated, driven by parameters that can be detected during the vegetative growth phase solely. CONCLUSIONS: Our findings suggest that selection for taller and fast-growing genotypes is likely to lead to an increase in floral bud productivity. It was also found that the final plant height and stem diameter are determined by 5 independent factors that can be used to maximize productivity through cultivation adjustments. The proposed prediction equation can facilitate the selection of prolific genotypes without the completion of a full cultivation cycle. Future studies that will associate genome-wide variation with plants morphological traits and cannabinoid profile will enable precise and accelerated breeding through genomic selection approaches.

Manipulation of Cannabinoid Biosynthesis via Transient RNAi Expression.

Cannabis sativa L. produces unique phytocannabinoids, which are used for their pharmaceutical benefits. To date, there are no reports of in vivo engineering targeting the cannabinoid biosynthesis genes to greater elucidate the role each of these genes play in synthesis of these medically important compounds. Reported here is the first modulation of cannabinoid biosynthesis genes using RNAi via agroinfiltration. Vacuum infiltrated leaf segments of the Cannbio-2 C. sativa strain, transfected with different RNAi constructs corresponding to THCAS, CBDAS, and CBCAS gene sequences, showed significant downregulation of all cannabinoid biosynthesis genes using real-time quantitative PCR. Using RNAi, significant off-targeting occurs resulting in the downregulation of highly homologous transcripts. Significant (p < 0.05) downregulation was observed for THCAS (92%), CBDAS (97%), and CBCAS (70%) using pRNAi-GG-CBDAS-UNIVERSAL. Significant (p < 0.05) upregulation of CBCAS (76%) and non-significant upregulation of THCAS (13%) were observed when transfected with pRNAi-GG-CBCAS, suggesting the related gene's ability to synthesize multiple cannabinoids. Using this approach, increased understanding of the relationship between cannabinoid biosynthesis genes can be further elucidated. This RNAi approach enables functional genomics screens for further reverse genetic studies as well as the development of designer cannabis strains with over-expression and/or downregulation of targeted cannabinoid biosynthesis genes. Functional genomics screens, such as these, will further provide insights into gene regulation of cannabinoid biosynthesis in Cannabis.

Metabolic Potential of Epichloë Endophytes for Host Grass Fungal Disease Resistance.

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.

Fungus-originated genes in the genomes of cereal and pasture grasses acquired through ancient lateral transfer.

Evidence for ancestral gene transfer between Epichloë fungal endophyte ancestors and their host grass species is described. From genomes of cool-season grasses (the Poeae tribe), two Epichloë-originated genes were identified through DNA sequence similarity analysis. The two genes showed 96% and 85% DNA sequence identities between the corresponding Epichloë genes. One of the genes was specific to the Loliinae sub-tribe. The other gene was more widely conserved in the Poeae and Triticeae tribes, including wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.). The genes were independently transferred during the last 39 million years. The transferred genes were expressed in plant tissues, presumably retaining molecular functions. Multiple gene transfer events between the specific plant and fungal lineages are unique. A range of cereal crops is included in the Poeae and Triticeae tribes, and the Loliinae sub-tribe is consisted of economically important pasture and forage crops. Identification and characterisation of the 'natural' adaptation transgenes in the genomes of cereals, and pasture and forage grasses, that worldwide underpin the production of major foods, such as bread, meat, and milk, may change the 'unnatural' perception status of transgenic and gene-edited plants.

Novel Antifungal Activity of Lolium-Associated Epichloë Endophytes.

Asexual Epichloë spp. fungal endophytes have been extensively studied for their functional secondary metabolite production. Historically, research mostly focused on understanding toxicity of endophyte-derived compounds on grazing livestock. However, endophyte-derived compounds also provide protection against invertebrate pests, disease, and other environmental stresses, which is important for ensuring yield and persistence of pastures. A preliminary screen of 30 strains using an in vitro dual culture bioassay identified 18 endophyte strains with antifungal activity. The novel strains NEA12, NEA21, and NEA23 were selected for further investigation as they are also known to produce alkaloids associated with protection against insect pests. Antifungal activity of selected endophyte strains was confirmed against three grass pathogens, Ceratobasidium sp., Dreschlera sp., and Fusarium sp., using independent isolates in an in vitro bioassay. NEA21 and NEA23 showed potent activity against Ceratobasidium sp. and NEA12 showed moderate inhibition against all three pathogens. Crude extracts from liquid cultures of NEA12 and NEA23 also inhibited growth of the phytopathogens Ceratobasidium sp. and Fusarium sp. and provided evidence that the compounds of interest are stable, constitutively expressed, and secreted. Comparative analysis of the in vitro and in planta metabolome of NEA12 and NEA23 using LCMS profile data revealed individual metabolites unique to each strain that are present in vitro and in planta. These compounds are the best candidates for the differential bioactivity observed for each strain. Novel endophyte strains show promise for endophyte-mediated control of phytopathogens impacting Lolium spp. pasture production and animal welfare.

Ergot alkaloid mycotoxins: physiological effects, metabolism and distribution of the residual toxin in mice.

The complex ergot alkaloids, ergovaline and ergotamine, cause dysregulation of physiological functions, characterised by vasoconstriction as well as thermoregulatory and cardiovascular effects in grazing livestock. To assess the effect of the mycotoxins, blood pressure and heart rate of male mice were measured, and metabolite profiling undertaken to determine relative abundances of both ergotamine and its metabolic products in body and brain tissue. Ergotamine showed similar cardiovascular effects to ergovaline, causing elevations in blood pressure and reduced heart rate. Bradycardia was preserved at low-levels of ergovaline despite no changes in blood pressure. Ergotamine was identified in kidney, liver and brainstem but not in other regions of the brain, which indicates region-specific effects of the toxin. The structural configuration of two biotransformation products of ergotamine were determined and identified in the liver and kidney, but not the brain. Thus, the dysregulation in respiratory, thermoregulatory, cardiac and vasomotor function, evoked by ergot alkaloids in animals observed in various studies, could be partially explained by dysfunction in the autonomic nervous system, located in the brainstem.

Development and Validation of a Phenotyping Computational Workflow to Predict the Biomass Yield of a Large Perennial Ryegrass Breeding Field Trial.

Increasing dry matter yield (DMY) is the most important objective in perennial ryegrass breeding programs. Current yield assessment methods like cutting are time-consuming and destructive, non-destructive measures such as scoring yield on single plants by visual inspection may be subjective. These assessments involve multiple measurements and selection procedures across seasons and years to evaluate biomass yield repeatedly. This contributes to the slow process of new cultivar development and commercialisation. This study developed and validated a computational phenotyping workflow for image acquisition, processing and analysis of spaced planted ryegrass and investigated sensor-based DMY yield estimation of individual plants through normalized difference vegetative index (NDVI) and ultrasonic plant height data extraction. The DMY of 48,000 individual plants representing 50 advanced breeding lines and commercial cultivars was accurately estimated at multiple harvests across the growing season. NDVI, plant height and predicted DMY obtained from aerial and ground-based sensors illustrated the variation within and between cultivars across different seasons. Combining NDVI and plant height of individual plants was a robust method to enable high-throughput phenotyping of biomass yield in ryegrass breeding. Similarly, the plot-level model indicated good to high-coefficients of determination (R 2) between the predicted and measured DMY across three seasons with R 2 between 0.19 and 0.81 and root mean square errors (RMSE) values ranging from 0.09 to 0.21 kg/plot. The model was further validated using a combined regression of the three seasons harvests. This study further sets a foundation for the application of sensor technologies combined with genomic studies that lead to greater rates of genetic gain in perennial ryegrass biomass yield.