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In the elderly, the coexistence of type 2 diabetes mellitus (T2DM) and frailty is frequent. Much has been described about pharmacological management and glycemic control goals. However, there is a knowledge gap in terms of the objectives and characteristics of interventions, especially nutritional ones, for this population. A scoping review was performed to document the objectives, characteristics, and results of nutritional interventions in older people with T2DM and frailty. The five-stage framework of Arksey and O’Malley was used, as was the PRISMA extension for scoping reviews. The results stand out for three trends, as follows: (1) experimental studies with multicomponent intervention physical exercise programs and nutritional programs based on educational processes or behavioral intervention; (2) observational studies with an association of the kind of diet assessed by scales and their relation to stages of frailty; (3) a review that updates recommendations on pharmacological and non-pharmacological, diet, exercise, management, as well as glucose control goals for diabetes in frail older persons. Finally, the evidence shows that management of T2DM in older adults with frailty requires goals and interventions tailored to their functional capacity and health condition. The exercise, diet, and education programs reviewed have demonstrated their effectiveness in improving physical performance, reducing the risk of frailty or progression to more advanced stages, and achieving better glycemic control.

Abstract 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.

Abstract 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.

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: FM~LV∗LV_Den∗NDVI had a determination of coefficient R2 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—FM~LV∗LV_Den. 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.

Some ocean worlds may sustain active, seafloor hydrothermal systems, but the characteristics and controls on fluid‐heat transport in these systems are not well understood. We developed three‐dimensional numerical simulations, using a ridge‐flank hydrothermal system on Earth as a reference, to test the influence of ocean world gravity on fluid and heat transport. Simulations represented the upper ∼4–5 km below the seafloor and explored ranges of: heat input at the base, aquifer thickness, depth, and permeability, and gravity values appropriate for Earth, Europa, and Enceladus. We tested when a hydrothermal siphon could be sustained and quantified consequent circulation temperatures, flow rates, and advective heat output. Calculations illustrate a trade‐off in energy between the reduction of buoyancy at lower gravity, which tends to reduce the primary forces driving fluid circulation, and the concomitant reduction in secondary convection, which consumes available energy. When a siphon was sustained under lower gravity, circulation temperatures tended to increase modestly (which should lead to more extensive geochemical reactions), whereas mass flow rates and advective heat output tended to be reduced. Deeper subseafloor circulation resulted in higher temperatures and flow rates, with a deeper, thin aquifer being more efficient in removing heat from the rocky interior. Water‐rock ratios were lower when gravity was lower, as was the efficiency of heat extraction, whereas the time required to circulate the volume of an ocean‐world's ocean through the seafloor increased. This may help to explain how small ocean worlds could sustain hydrothermal circulation for a long time despite limited heat sources. Plain Language Summary: Ocean worlds are planetary bodies that have a liquid ocean, often under an icy shell or within the rocky interior. In Earth's solar system, several moons of Jupiter and Saturn are ocean worlds. Some ocean worlds are thought to have hydrothermal circulation, where water, rocks, and heat combine to drive fluids in and out of the seafloor. Hydrothermal circulation would impact the chemistry of the water and rock of ocean worlds, and could help life to develop deep below the icy surface. This study shows results from computer simulations of hydrothermal circulation, based on a well‐understood system on Earth, to measure the influence of lower gravity at values appropriate for ocean worlds smaller than Earth. The simulations with ocean world (lower) gravity result in fluid circulation much like that occurring on and below Earth's seafloor, but with several important differences. Lower gravity reduces buoyancy, so fluids don't become as light when heated, and this reduces flow rates. This can raise temperatures in the circulating fluid, which could allow more extensive chemical reactions, perhaps including those that sustain life. Lower flow means less heat transport, and this could help these flows to last longer in an ocean world. Key Points: Outcrop‐to‐outcrop hydrothermal circulation, driven by heating from below, can be sustained under ocean‐word gravityLower gravity tends to generate higher circulation temperatures, lower mass fluxes, and reduced heat outputLower gravity also results in lower water‐rock ratios and should create more evolved fluids and a longer circulation time [ABSTRACT FROM AUTHOR]

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.

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.

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.

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.

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

Molecular characterization of genetically modified plants can provide crucial information for the development of detection and identification methods, to comply with traceability, and labeling requirements prior to commercialization. Detailed description of the genetic modification was previously a challenging step in the safety assessment, since it required the use of laborious and time-consuming techniques. In this study an accurate, simple, and fast method was developed for molecular characterization of genetically modified (GM) plants, following a user-friendly workflow for researchers with limited bioinformatic capabilities. Three GM events from a diverse array of crop species—perennial ryegrass, white clover, and canola—were used to test the approach that exploits long-read sequencing by the MinION device, from Oxford Nanopore Technologies. The method delivered a higher degree of resolution of the transgenic events within the host genome than has previously been possible with the standard Illumina short-range sequencing strategies. The flanking sequences, copy number, and presence of backbone sequences, and overall transgene insertion structure were determined for each of the plant genomes, with the additional identification of moderate-sized secondary insertions that would have previously been missed. The proposed workflow takes only about 1 week from DNA extraction to analyzed result, and the method will complement the existing approaches for molecular characterization of GM plants, since it makes the process faster, simpler, and more cost-effective.

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.

Cannabis is a diploid species (2n = 20), the estimated haploid genome sizes of the female and male plants using flow cytometry are 818 and 843 Mb respectively. Although the genome of Cannabis has been sequenced (from hemp, wild and high-THC strains), all assemblies have significant gaps. In addition, there are inconsistencies in the chromosome numbering which limits their use. A new comprehensive draft genome sequence assembly (∼900 Mb) has been generated from the medicinal cannabis strain Cannbio-2, that produces a balanced ratio of cannabidiol and delta-9-tetrahydrocannabinol using long-read sequencing. The assembly was subsequently analysed for completeness by ordering the contigs into chromosome-scale pseudomolecules using a reference genome assembly approach, annotated and compared to other existing reference genome assemblies. The Cannbio-2 genome sequence assembly was found to be the most complete genome sequence available based on nucleotides assembled and BUSCO evaluation in Cannabis sativa with a comprehensive genome annotation. The new draft genome sequence is an advancement in Cannabis genomics permitting pan-genome analysis, genomic selection as well as genome editing.

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.

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 (R2) between the predicted and measured DMY across three seasons with R2 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.

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 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.

Food security is one of major concerns for the growing global population. Modern agricultural biotechnologies, such as genetic modification, are a possible solution through enabling an increase of production, more efficient use of natural resources, and reduced environmental impacts. However, new crop varieties with altered genetic materials may be subjected to safety assessments to fulfil the regulatory requirements, prior to marketing. The aim of the assessment is to evaluate the impact of products from the new crop variety on human, animal, and the environmental health. Although, many studies on the risk assessment of genetically modified (GM) food have been published, little consideration to GM feedstuff has been given, despite that between 70 to 90% of all GM crops and their biomass are used as animal feed. In addition, in some GM plants such as forages that are only used for animal feeds, the assessment of the genetic modification may be of relevance only to livestock feeding. In this article, the regulatory framework of GM crops intended for animal feed is reviewed using the available information on GM food as the baseline. Although, the majority of techniques used for the safety assessment of GM food can be used in GM feed, many plant parts used for livestock feeding are inedible to humans. Therefore, the concentration of novel proteins in different plant tissues and level of exposure to GM feedstuff in the diet of target animals should be considered. A further development of specific methodologies for the assessment of GM crops intended for animal consumption is required, in order to provide a more accurate and standardized assessment to the GM feed safety.

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.

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.

Abstract Molecular characterisation has convincingly demonstrated some types of horizontal gene transfer in eukaryotes, but nuclear gene transfer between distantly related eukaryotic groups appears to have been rare. For angiosperms (flowering plants), nuclear gene transfer events identified to date have been confined to genes originating from prokaryotes or other plant species. In this report, evidence for ancient horizontal transfer of a fungal nuclear gene, encoding a ß-1,6-glucanase enzyme for fungal cell wall degradation, into an angiosperm lineage is presented for the first time. The gene was identified from de novo sequencing and assembly of the genome and transcriptome of perennial ryegrass, a cool-season grass species. Molecular analysis confirmed the presence of the complete gene in the genome of perennial ryegrass. No corresponding sequence was found in other plant species, apart from members of the Poeae sub-tribes Loliinae and Dactylidinae. Evidence suggests that a common ancestor of the two sub-tribes acquired the gene from a species ancestral to contemporary grass-associated fungal endophytes around 9–13 million years ago. This first report of horizontal transfer of a nuclear gene from a taxonomically distant eukaryote to modern flowering plants provides evidence for a novel adaptation mechanism in angiosperms.

Breeding schemes that utilize modern breeding methods like genomic selection (GS) and speed breeding (SB) have the potential to accelerate genetic gain for different crops. We investigated through stochastic computer simulation the advantages and disadvantages of adopting both GS and SB (SpeedGS) into commercial breeding programs for allogamous crops. In addition, we studied the effect of omitting one or two selection stages from the conventional phenotypic scheme on GS accuracy, genetic gain, and inbreeding. As an example, we simulated GS and SB for five traits (heading date, forage yield, seed yield, persistency, and quality) with different genetic architectures and heritabilities (0.7, 0.3, 0.4, 0.1, and 0.3; respectively) for a tall fescue breeding program. We developed a new method to simulate correlated traits with complex architectures of which effects can be sampled from multiple distributions, e.g. to simulate the presence of both minor and major genes. The phenotypic selection scheme required 11 years, while the proposed SpeedGS schemes required four to nine years per cycle. Generally, SpeedGS schemes resulted in higher genetic gain per year for all traits especially for traits with low heritability such as persistency. Our results showed that running more SB rounds resulted in higher genetic gain per cycle when compared to phenotypic or GS only schemes and this increase was more pronounced per year when cycle time was shortened by omitting cycle stages. While GS accuracy declined with additional SB rounds, the decline was less in round three than in round two, and it stabilized after the fourth SB round. However, more SB rounds resulted in higher inbreeding rate, which could limit long-term genetic gain. The inbreeding rate was reduced by approximately 30% when generating the initial population for each cycle through random crosses instead of generating half-sib families. Our study demonstrated a large potential for additional genetic gain from combining GS and SB. Nevertheless, methods to mitigate inbreeding should be considered for optimal utilization of these highly accelerated breeding programs.

Implementation of molecular biotechnology, such as transgenic technologies, in forage species can improve agricultural profitability through achievement of higher productivity, better use of resources such as soil nutrients, water, or light, and reduced environmental impact. Development of detection and quantification techniques for genetically modified plants are necessary to comply with traceability and labeling requirements prior to regulatory approval for release. Real-time PCR has been the standard method used for detection and quantification of genetically modified events, and droplet digital PCR is a recent alternative technology that offers a higher accuracy. Evaluation of both technologies was performed using a transgenic high-energy forage grass as a case study. Two methods for detection and quantification of the transgenic cassette, containing modified fructan biosynthesis genes, and a selectable marker gene, hygromycin B phosphotransferase used for transformation, were developed. Real-time PCR was assessed using two detection techniques, SYBR Green I and fluorescent probe-based methods. A range of different agricultural commodities were tested including fresh leaves, tillers, seeds, pollen, silage and hay, simulating a broad range of processed agricultural commodities that are relevant in the commercial use of genetically modified pastures. The real-time and droplet digital PCR methods were able to detect both exogenous constructs in all agricultural products. However, a higher sensitivity and repeatability in transgene detection was observed with the droplet digital PCR technology. Taking these results more broadly, it can be concluded that the droplet digital PCR technology provides the necessary resolution for quantitative analysis and detection, allowing absolute quantification of the target sequence at the required limits of detection across all jurisdictions globally. The information presented here provides guidance and resources for pasture-based biotechnology applications that are required to comply with traceability requirements.

Whole genome sequencing offers genome wide, unbiased markers, and inexpensive library preparation. With the cost of sequencing decreasing rapidly, many plant genomes of modest size are amenable to skim whole genome resequencing (skim WGR). The use of skim WGR in diverse sample sets without the use of imputation was evaluated in silico in 149 canola samples representative of global diversity. Fastq files with an average of 10x coverage of the reference genome were used to generate skim samples representing 0.25x, 0.5x, 1x, 2x, 3x, 4x, and 5x sequencing coverage. Applying a pre-defined list of SNPs versus de novo SNP discovery was evaluated. As skim WGR is expected to result in some degree of insufficient allele sampling, all skim coverage levels were filtered at a range of minimum read depths from a relaxed minimum read depth of 2 to a stringent read depth of 5, resulting in 28 list-based SNP sets. As a broad recommendation, genotyping pre-defined SNPs between 1x and 2x coverage with relatively stringent depth filtering is appropriate for a diverse sample set of canola due to a balance between marker number, sufficient accuracy, and sequencing cost, but depends on the intended application. This was experimentally examined in two sample sets with different genetic backgrounds: 1x coverage of 1,590 individuals from 84 Australian spring type four-parent crosses aimed at maximizing diversity as well as one commercial F1 hybrid, and 2x coverage of 379 doubled haploids (DHs) derived from a subset of the four-parent crosses. To determine optimal coverage in a simpler genetic background, the DH sample sequence coverage was further down sampled in silico. The flexible and cost-effective nature of the protocol makes it highly applicable across a range of species and purposes.