Modified fructan accumulation through overexpression of wheat fructan biosynthesis pathway fusion genes Ta1SST:Ta6SFT.

BACKGROUND: Fructans are water-soluble carbohydrates that accumulate in wheat and are thought to contribute to a pool of stored carbon reserves used in grain filling and tolerance to abiotic stress. RESULTS: In this study, transgenic wheat plants were engineered to overexpress a fusion of two fructan biosynthesis pathway genes, wheat sucrose: sucrose 1-fructosyltransferase (Ta1SST) and wheat sucrose: fructan 6-fructosyltransferase (Ta6SFT), regulated by a wheat ribulose-1,5-bisphosphate carboxylase/oxygenase small subunit (TaRbcS) gene promoter. We have shown that T4 generation transgene-homozygous single-copy events accumulated more fructan polymers in leaf, stem and grain when compared in the same tissues from transgene null lines. Under water-deficit (WD) conditions, transgenic wheat plants showed an increased accumulation of fructan polymers with a high degree of polymerisation (DP) when compared to non-transgenic plants. In wheat grain of a transgenic event, increased deposition of particular fructan polymers such as, DP4 was observed. CONCLUSIONS: This study demonstrated that the tissue-regulated expression of a gene fusion between Ta1SST and Ta6SFT resulted in modified fructan accumulation in transgenic wheat plants and was influenced by water-deficit stress conditions.

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.

An image dataset of diverse safflower (Carthamus tinctorius L.) genotypes for salt response phenotyping.

This article describes a dataset of high-resolution visible-spectrum images of safflower (Carthamus tinctorius L.) plants obtained from a LemnaTec Scanalyser automated phenomics platform along with the associated image analysis output and manually acquired biomass data. This series contains 1832 images of 200 diverse safflower genotypes, acquired at the Plant Phenomics Victoria, Horsham, Victoria, Australia. Two Prosilica GT RGB (red-green-blue) cameras were used to generate 6576 × 4384 pixel portable network graphic (PNG) images. Safflower genotypes were either subjected to a salt treatment (250 mM NaCl) or grown as a control (0 mM NaCl) and imaged daily from 15 to 36 days after sowing. Each snapshot consists of four images collected at a point in time; one of which is taken from above (top-view) and the remainder from the side at either 0°, 120° or 240°. The dataset also includes analysis output quantifying traits and describing phenotypes, as well as manually collected biomass and leaf ion content data. The usage of the dataset is already demonstrated in Thoday-Kennedy et al. (2021) [1]. This dataset describes the early growth differences of diverse safflower genotypes and identified genotypes tolerant or susceptible to salinity stress. This dataset provides detailed image analysis parameters for phenotyping a large population of safflower that can be used for the training of image-based trait identification pipelines for a wide range of crop species.

Australian native Glycine clandestina seed microbiota hosts a more diverse bacterial community than the domesticated soybean Glycine max.

BACKGROUND: Plant microbiome composition has been demonstrated to change during the domestication of wild plants and it is suggested that this has resulted in loss of plant beneficial microbes. Recently, the seed microbiome of native plants was demonstrated to harbour a more diverse microbiota and shared a common core microbiome with modern cultivars. In this study the composition of the seed-associated bacteria of Glycine clandestina is compared to seed-associated bacteria of Glycine max (soybean). RESULTS: The seed microbiome of the native legume Glycine clandestina (crop wild relative; cwr) was more diverse than that of the domesticated Glycine max and was dominated by the bacterial class Gammaproteobacteria. Both the plant species (cwr vs domesticated) and individual seed accessions were identified as the main driver for this diversity and composition of the microbiota of all Glycine seed lots, with the effect of factor "plant species" exceeded that of "geographical location". A core microbiome was identified between the two Glycine species. A high percentage of the Glycine microbiome was unculturable [G. clandestina (80.8%) and G. max (75.5%)] with only bacteria of a high relative abundance being culturable under the conditions of this study. CONCLUSION: Our results provided novel insights into the structure and diversity of the native Glycine clandestina seed microbiome and how it compares to that of the domesticated crop Glycine max. Beyond that, it also increased our knowledge of the key microbial taxa associated with the core Glycine spp. microbiome, both wild and domesticated. The investigation of this commonality and diversity is a valuable and essential tool in understanding the use of native Glycine spp. for the discovery of new microbes that would be of benefit to domesticated Glycine max cultivars or any other economically important crops. This study has isolated microbes from a crop wild relative that are now available for testing in G. max for beneficial phenotypes.

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.

Corrigendum: Implications of Seed Vault Storage Strategies for Conservation of Seed Bacterial Microbiomes.

[This corrects the article DOI: 10.3389/fmicb.2021.784796.].

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.

The Role of Soil Microbial Diversity in the Conservation of Native Seed Bacterial Microbiomes.

Research into understanding the structure, composition and vertical transmission of crop seed microbiomes has intensified, although there is much less research into the seed microbiomes of crop wild relatives. Our previous study showed that the standard seed storage procedures (e.g., seed drying and storage temperature) can influence the seed microbiome of domesticated Glycine max. In this study, we characterized the seed microbiota of Glycine clandestina, a perennial wild relative of soybean (G. max (L.) Merr.) to expand our understanding about the effect of other storage procedures such as the periodic regeneration of seed stocks to bulk up seed numbers and secure viability on the seed microbiome of said seed. The G. clandestina microbiota was analysed from Generation 1 (G1) and Generation 2 (G2) seed and from mature plant organs grown in two different soil treatments T (treatment [native soil + potting mix]) and C (control [potting mix only]). Our dataset showed that soil microbiota had a strong influence on next generation seed microbiota, with an increased contribution of root microbiota by 90% and seed transmissibility by 36.3% in G2 (T) seed. Interestingly, the G2 seed microbiota primarily consisted of an initially low abundance of taxa present in G1 seed. Overall, our results indicate that seed regeneration can affect the seed microbiome composition and using native soil from the location of the source plant can enhance the conservation of the native seed microbiota.

Protoplast Isolation, Transformation, and Regeneration for Forage and Turf Grasses.

Forage and turf grasses are widely grown and contribute significantly to sustainable agriculture. This chapter describes a protocol for protoplast transformation and plant regeneration for major forage and turf grass species, including tall fescue, red fescue, meadow fescue, perennial ryegrass, and Italian ryegrass. Embryogenic calli induced from caryopsis were used to establish embryogenic cell suspension cultures. Protoplasts were isolated from embryogenic suspension cultures and used for direct gene transfer. Chimeric genes were introduced into protoplasts by polyethylene glycol treatment. Upon selection with antibiotics or herbicide, resistant calli were obtained and transgenic plants were regenerated from these calli.

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.

Automated hyperspectral vegetation index derivation using a hyperparameter optimisation framework for high-throughput plant phenotyping.

Hyperspectral vegetation indices (VIs) are widely deployed in agriculture remote sensing and plant phenotyping to estimate plant biophysical and biochemical traits. However, existing VIs consist mainly of simple two-band indices that limit the net performance and often do not generalise well for traits other than those for which they were originally designed. We present an automated hyperspectral vegetation index (AutoVI) system for the rapid generation of novel two- to six-band trait-specific indices in a streamlined process covering model selection, optimisation and evaluation, driven by the tree parzen estimator algorithm. Its performance was tested in generating novel indices to estimate chlorophyll and sugar contents in wheat. Results showed that AutoVI can rapidly generate complex novel VIs (at least a four-band index) that correlated strongly (R2  > 0.8) with measured chlorophyll and sugar contents in wheat. Automated hyperspectral vegetation index-derived indices were used as features in simple and stepwise multiple linear regressions for chlorophyll and sugar content estimation, and outperformed the results achieved with the existing 47 VIs and those provided using partial least squares regression. The AutoVI system can deliver novel trait-specific VIs readily adoptable to high-throughput plant phenotyping platforms and should appeal to plant scientists and breeders. A graphical user interface for the AutoVI is provided here.

Implications of Seed Vault Storage Strategies for Conservation of Seed Bacterial Microbiomes.

Global seed vaults are important, as they conserve plant genetic resources for future breeding to improve crop yield and quality and to overcome biotic and abiotic stresses. However, little is known about the impact of standard storage procedures, such as seed drying and cold storage on the seed bacterial community, and the ability to recover seed-associated bacteria after storage. In this study, soybean [Glycine max (L.) Merr.] seeds were analyzed to characterize changes in the bacterial community composition and culturability under varying storage conditions. The G. max bacterial microbiome was analyzed from undried seed, dried seed, and seed stored for 0, 3, 6, and 14months. Storage temperatures consisted of -20°C, 4°C, and room temperature (RT), with -20°C being commonly used in seed storage vaults globally. The seed microbiome of G. max was dominated by Gammaproteobacteria under all conditions. Undried seed was dominated by Pantoea (33.9%) and Pseudomonas (51.1%); however, following drying, the abundance of Pseudomonas declined significantly (0.9%), Pantoea increased significantly (73.6%), and four genera previously identified including Pajaroellobacter, Nesterenkonia, env.OPS_17, and Acidibacter were undetectable. Subsequent storage at RT, 4, or -20°C maintained high-abundance Genera at the majority of time points, although RT caused greater fluctuations in abundances. For many of the low-abundance Genera, storage at -20°C resulted in their gradual disappearance, whereas storage at 4°C or RT resulted in their more rapid disappearance. The changes in seed bacterial composition were reflected by cultured bacterial taxa obtained from the stored G. max seed. The main taxa were largely culturable and had similar relative abundance, while many, but not all, of the low-abundance taxa were also culturable. Overall, these results indicate that the initial seed drying affects the seed bacterial composition, suggesting that microbial isolation prior to seed drying is recommended to conserve these microbes. The standard seed storage condition of -20°C is most suitable for conservation of the bacterial seed microbiome, as this storage temperature slows down the loss of seed bacterial diversity over longer time periods, particularly low-abundance taxa.

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.