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.

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.

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.

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.

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.

The genome of Cleistogenes songorica provides a blueprint for functional dissection of dimorphic flower differentiation and drought adaptability.

Cleistogenes songorica (2n = 4x = 40) is a desert grass with a unique dimorphic flowering mechanism and an ability to survive extreme drought. Little is known about the genetics underlying drought tolerance and its reproductive adaptability. Here, we sequenced and assembled a high-quality chromosome-level C. songorica genome (contig N50 = 21.28 Mb). Complete assemblies of all telomeres, and of ten chromosomes were derived. C. songorica underwent a recent tetraploidization (~19 million years ago) and four major chromosomal rearrangements. Expanded genes were significantly enriched in fatty acid elongation, phenylpropanoid biosynthesis, starch and sucrose metabolism, and circadian rhythm pathways. By comparative transcriptomic analysis we found that conserved drought tolerance related genes were expanded. Transcription of CsMYB genes was associated with differential development of chasmogamous and cleistogamous flowers, as well as drought tolerance. Furthermore, we found that regulation modules encompassing miRNA, transcription factors and target genes are involved in dimorphic flower development, validated by overexpression of CsAP2_9 and its targeted miR172 in rice. Our findings enable further understanding of the mechanisms of drought tolerance and flowering in C. songorica, and provide new insights into the adaptability of native grass species in evolution, along with potential resources for trait improvement in agronomically important species.

Digital PCR (dPCR) and qPCR mediated determination of transgene copy number in the forage legume white clover (Trifolium repens).

Obtaining data on transgene copy number is an integral step in the generation of transgenic plants. Techniques such as Southern blot, segregation analysis, and quantitative PCR (qPCR) have routinely been used for this task, in a range of species. More recently, use of Digital PCR (dPCR) has become prevalent, with a measurement accuracy higher than qPCR reported. Here, the relative merits of qPCR and dPCR for transgene copy number estimation in white clover were investigated. Furthermore, given that single copy reference genes are desirable for estimating gene copy number by relative quantification, and that no single-copy genes have been reported in this species, a search and evaluation of suitable reference genes in white clover was undertaken. Results demonstrated a higher accuracy of dPCR relative to qPCR for copy number estimation in white clover. Two genes, Pyruvate dehydrogenase (PDH), and an ATP-dependent protease, identified as single-copy genes, were used as references for copy number estimation by relative quantification. Identification of single-copy genes in white clover will enable the application of relative quantification for copy number estimation of other genes or transgenes in the species. The results generated here validate the use of dPCR as a reliable strategy for transgene copy number estimation in white clover, and provide resources for future copy number studies in this species.

High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response.

The development of crop varieties with higher nitrogen use efficiency is crucial for sustainable crop production. Combining high-throughput genotyping and phenotyping will expedite the discovery of novel alleles for breeding crop varieties with higher nitrogen use efficiency. Digital and hyperspectral imaging techniques can efficiently evaluate the growth, biophysical, and biochemical performance of plant populations by quantifying canopy reflectance response. Here, these techniques were used to derive automated phenotyping of indicator biomarkers, biomass and chlorophyll levels, corresponding to different nitrogen levels. A detailed description of digital and hyperspectral imaging and the associated challenges and required considerations are provided, with application to delineate the nitrogen response in wheat. Computational approaches for spectrum calibration and rectification, plant area detection, and derivation of vegetation index analysis are presented. We developed a novel vegetation index with higher precision to estimate chlorophyll levels, underpinned by an image-processing algorithm that effectively removed background spectra. Digital shoot biomass and growth parameters were derived, enabling the efficient phenotyping of wheat plants at the vegetative stage, obviating the need for phenotyping until maturity. Overall, our results suggest value in the integration of high-throughput digital and spectral phenomics for rapid screening of large wheat populations for nitrogen response.

Genome-wide association reveals a complex architecture for rust resistance in 2300 worldwide bread wheat accessions screened under various Australian conditions.

We utilized 2300 wheat accessions including worldwide landraces, cultivars and primary synthetic-derived germplasm with three Australian cultivars: Annuello, Yitpi and Correll, to investigate field-based resistance to leaf (Lr) rust, stem (Sr) rust and stripe (Yr) rust diseases across a range of Australian wheat agri-production zones. Generally, the resistance in the modern Australian cultivars, synthetic derivatives, South and North American materials outperformed other geographical subpopulations. Different environments for each trait showed significant correlations, with average r values of 0.53, 0.23 and 0.66 for Lr, Sr and Yr, respectively. Single-trait genome-wide association studies (GWAS) revealed several environment-specific and multi-environment quantitative trait loci (QTL). Multi-trait GWAS confirmed a cluster of Yr QTL on chromosome 3B within a 4.4-cM region. Linkage disequilibrium and comparative mapping showed that at least three Yr QTL exist within the 3B cluster including the durable rust resistance gene Yr30. An Sr/Lr QTL on chromosome 3D was found mainly in the synthetic-derived germplasm from Annuello background which is known to carry the Agropyron elongatum 3D translocation involving the Sr24/Lr24 resistance locus. Interestingly, estimating the SNP effects using a BayesR method showed that the correlation among the highest 1% of QTL effects across environments (excluding GWAS QTL) had significant correlations, with average r values of 0.26, 0.16 and 0.55 for Lr, Sr and Yr, respectively. These results indicate the importance of small effect QTL in achieving durable rust resistance which can be captured using genomic selection.

Identification and Distribution of Novel Metabolites of Lolitrem B in Mice by High-Resolution Mass Spectrometry.

Lolitrem B is the most potent indole-diterpene mycotoxin produced by Epichloë festucae var. lolii (termed LpTG-1), with severe intoxication cases reported in livestock. To date, there are no in vivo metabolism studies conducted for the mycotoxin. A mouse model assay established for assessing toxicity of indole-diterpenes was used to investigate metabolic products of lolitrem B. Mice were administered lolitrem B at 0.5 and 2.0 mg/kg body weight (b.wt) intraperitoneally before body and brain tissues were collected at 6 h and 24 h post-treatment. Samples were cryoground and subjected to a biphasic or monophasic extraction. The aqueous and lipophilic phases were analysed using liquid chromatography high-resolution mass spectrometry (LC-HRMS); data analysis was performed with Compound Discoverer™ software. A total of 10 novel phase I metabolic products were identified in the lipophilic phase and their distribution in the liver, kidney and various brain regions are described. The biotransformation products of lolitrem B were found to be present in low levels in the brain. Based on structure-activity postulations, six of these may contribute towards the protracted tremors exhibited by lolitrem B-exposed animals.

Genetic modification of asexual Epichloë endophytes with the perA gene for peramine biosynthesis.

Development of grass-endophyte associations with minimal or no detrimental effects in combination with beneficial characteristics is important for pastoral agriculture. The feasibility of enhancing production of an endophyte-derived beneficial alkaloid through introduction of an additional gene copy was assessed in a proof-of-concept study. Sexual and asexual Epichloë species that form symbiotic associations with cool-season grasses of the Poaceae sub-family Pooideae produce bioactive alkaloids that confer resistance to herbivory by a number of organisms. Of these, peramine is thought to be crucial for protection of perennial ryegrass (Lolium perenne L.) from the Argentinian stem weevil, an economically important exotic pest in New Zealand, contributing significantly to pasture persistence. A single gene (perA) has been identified as solely responsible for peramine biosynthesis and is distributed widely across Epichloë taxa. In the present study, a functional copy of the perA gene was introduced into three recipient endophyte genomes by Agrobacterium tumefaciens-mediated transformation. The target strains included some that do not produce peramine, and others containing different perA gene copies. Mitotically stable transformants generated from all three endophyte strains were able to produce peramine in culture and in planta at variable levels. In summary, this study provides an insight into the potential for artificial combinations of alkaloid biosynthesis in a single endophyte strain through transgenesis, as well as the possibility of using novel genome editing techniques to edit the perA gene of non-peramine producing strains.

A Multiple Protease Strategy to Optimise the Shotgun Proteomics of Mature Medicinal Cannabis Buds.

Earlier this year we published a method article aimed at optimising protein extraction from mature buds of medicinal cannabis for trypsin-based shotgun proteomics (Vincent, D., et al. Molecules 2019, 24, 659). We then developed a top-down proteomics (TDP) method (Vincent, D., et al. Proteomes 2019, 7, 33). This follow-up study aims at optimising the digestion of medicinal cannabis proteins for identification purposes by bottom-up and middle-down proteomics (BUP and MDP). Four proteases, namely a mixture of trypsin/LysC, GluC, and chymotrypsin, which target different amino acids (AAs) and therefore are orthogonal and cleave proteins more or less frequently, were tested both on their own as well as sequentially or pooled, followed by nLC-MS/MS analyses of the peptide digests. Bovine serum albumin (BSA, 66 kDa) was used as a control of digestion efficiency. With this multiple protease strategy, BSA was reproducibly 97% sequenced, with peptides ranging from 0.7 to 6.4 kD containing 5 to 54 AA residues with 0 to 6 miscleavages. The proteome of mature apical buds from medicinal cannabis was explored more in depth with the identification of 27,123 peptides matching 494 unique accessions corresponding to 229 unique proteins from Cannabis sativa and close relatives, including 130 (57%) additional annotations when the list is compared to that of our previous BUP study (Vincent, D., et al. Molecules 2019, 24, 659). Almost half of the medicinal cannabis proteins were identified with 100% sequence coverage, with peptides composed of 7 to 91 AA residues with up to 9 miscleavages and ranging from 0.6 to 10 kDa, thus falling into the MDP domain. Many post-translational modifications (PTMs) were identified, such as oxidation, phosphorylations, and N-terminus acetylations. This method will pave the way for deeper proteome exploration of the reproductive organs of medicinal cannabis, and therefore for molecular phenotyping within breeding programs.

Optimisation of Protein Extraction from Medicinal Cannabis Mature Buds for Bottom-Up Proteomics.

Medicinal cannabis is used to relieve the symptoms of certain medical conditions, such as epilepsy. Cannabis is a controlled substance and until recently was illegal in many jurisdictions. Consequently, the study of this plant has been restricted. Proteomics studies on Cannabis sativa reported so far have been primarily based on plant organs and tissues other than buds, such as roots, hypocotyl, leaves, hempseeds and flour. As far as we know, no optimisation of protein extraction from cannabis reproductive tissues has been attempted. Therefore, we set out to assess different protein extraction methods followed by mass spectrometry-based proteomics to recover, separate and identify the proteins of the reproductive organs of medicinal cannabis, apical buds and isolated trichomes. Database search following shotgun proteomics was limited to protein sequences from C. sativa and closely related species available from UniprotKB. Our results demonstrate that a buffer containing the chaotrope reagent guanidine hydrochloride recovers many more proteins than a urea-based buffer. In combination with a precipitation with trichloroacetic acid, such buffer proved optimum to identify proteins using a trypsin digestion followed by nano-liquid chromatography tandem mass spectrometry (nLC-MS/MS) analyses. This is validated by focusing on enzymes involved in the phytocannabinoid pathway.

Genotyping-by-sequencing through transcriptomics: implementation in a range of crop species with varying reproductive habits and ploidy levels.

The application of genomics in crops has the ability to significantly improve genetic gain for agriculture. Many marker-dense tools have been developed, but few have seen broad adoption in plant genomics due to issues of significant variations of genome size, levels of ploidy, single nucleotide polymorphism (SNP) frequency and reproductive habit. When combined with limited breeding activities, small research communities and scant sequence resources, the suitability of popular systems is often suboptimal and routinely fails to effectively balance cost-effectiveness and sample throughput. Genotyping-by-sequencing (GBS) encompasses a range of protocols including resequencing of the transcriptome. This study describes a skim GBS-transcriptomics (GBS-t) approach developed to be broadly applicable, cost-effective and high-throughput while still assaying a significant number of SNP loci. A range of crop species with differing levels of ploidy and degree of inbreeding/outbreeding were chosen, including perennial ryegrass, a diploid outbreeding forage grass; phalaris, a putative segmental allotetraploid outbreeding forage grass; lentil, a diploid inbreeding grain legume; and canola, an allotetraploid partially outbreeding oilseed. GBS-t was validated as a simple and largely automated, cost-effective method which generates sufficient SNPs (from 89 738 to 231 977) with acceptable levels of missing data and even genome coverage from c. 3 million sequence reads per sample. GBS-t is therefore a broadly applicable system suitable for many crops, offering advantages over other systems. The correct choice of subsequent sequence analysis software is important, and the bioinformatics process should be iterative and tailored to the specific challenges posed by ploidy variation and extent of heterozygosity.

Zinc finger nuclease-mediated precision genome editing of an endogenous gene in hexaploid bread wheat (Triticum aestivum) using a DNA repair template.

Sequence-specific nucleases have been used to engineer targeted genome modifications in various plants. While targeted gene knockouts resulting in loss of function have been reported with relatively high rates of success, targeted gene editing using an exogenously supplied DNA repair template and site-specific transgene integration has been more challenging. Here, we report the first application of zinc finger nuclease (ZFN)-mediated, nonhomologous end-joining (NHEJ)-directed editing of a native gene in allohexaploid bread wheat to introduce, via a supplied DNA repair template, a specific single amino acid change into the coding sequence of acetohydroxyacid synthase (AHAS) to confer resistance to imidazolinone herbicides. We recovered edited wheat plants having the targeted amino acid modification in one or more AHAS homoalleles via direct selection for resistance to imazamox, an AHAS-inhibiting imidazolinone herbicide. Using a cotransformation strategy based on chemical selection for an exogenous marker, we achieved a 1.2% recovery rate of edited plants having the desired amino acid change and a 2.9% recovery of plants with targeted mutations at the AHAS locus resulting in a loss-of-function gene knockout. The latter results demonstrate a broadly applicable approach to introduce targeted modifications into native genes for nonselectable traits. All ZFN-mediated changes were faithfully transmitted to the next generation.

Exploitation of data from breeding programs supports rapid implementation of genomic selection for key agronomic traits in perennial ryegrass.

KEY MESSAGE: Exploitation of data from a ryegrass breeding program has enabled rapid development and implementation of genomic selection for sward-based biomass yield with a twofold-to-threefold increase in genetic gain. Genomic selection, which uses genome-wide sequence polymorphism data and quantitative genetics techniques to predict plant performance, has large potential for the improvement in pasture plants. Major factors influencing the accuracy of genomic selection include the size of reference populations, trait heritability values and the genetic diversity of breeding populations. Global diversity of the important forage species perennial ryegrass is high and so would require a large reference population in order to achieve moderate accuracies of genomic selection. However, diversity of germplasm within a breeding program is likely to be lower. In addition, de novo construction and characterisation of reference populations are a logistically complex process. Consequently, historical phenotypic records for seasonal biomass yield and heading date over a 18-year period within a commercial perennial ryegrass breeding program have been accessed, and target populations have been characterised with a high-density transcriptome-based genotyping-by-sequencing assay. Ability to predict observed phenotypic performance in each successive year was assessed by using all synthetic populations from previous years as a reference population. Moderate and high accuracies were achieved for the two traits, respectively, consistent with broad-sense heritability values. The present study represents the first demonstration and validation of genomic selection for seasonal biomass yield within a diverse commercial breeding program across multiple years. These results, supported by previous simulation studies, demonstrate the ability to predict sward-based phenotypic performance early in the process of individual plant selection, so shortening the breeding cycle, increasing the rate of genetic gain and allowing rapid adoption in ryegrass improvement programs.

Evaluation of endophyte toxin production and its interaction with transgenic perennial ryegrass (Lolium perenne L.) with altered expression of fructosyltransferases.

Alkaloid concentration of perennial ryegrass herbage is affected by endophyte strain and host plant genotype. However, previous studies suggest that associations between host and endophyte also depends on environmental conditions, especially those affecting nutrient reserves and that water-soluble carbohydrate (WSC) concentration of perennial ryegrass plants may influence grass-endophyte associations. In this study a single transgenic event, with altered expression of fructosyltransferase genes to produce high WSC and biomass, has been crossed into a range of cultivar backgrounds with varying Epichloë endophyte strains. The effect of the association between the transgenic trait and alkaloid production was assessed and compared with transgene free control populations. In the vast-majority of comparisons there was no significant difference between alkaloid concentrations of transgenic and non-transgenic plants within the same cultivar and endophyte backgrounds. There was no significant difference between GOI+ (gene of interest positive) and GOI- (gene of interest negative) populations in Janthritrem response. Peramine concentration was not different between GOI+ and GOI- for 10 of the 12 endophytes-cultivar combinations. Cultivar Trojan infected with NEA6 and Alto with SE (standard endophyte) exhibited higher peramine and lolitrem B (only for Alto SE) concentration, in the control GOI- compared with GOI+. Similarly, cultivar Trojan infected with NEA6 and Alto with NEA3 presented higher ergovaline concentration in GOI-. Differences in alkaloid concentration may be attributable to an indirect effect in the modulation of fungal biomass. These results conclude that the presence of this transgenic insertion, does not alter the risk (toxicity) of the endophyte-grass associations. Endophyte-host interactions are complex and further research into associations with high WSC plant should be performed in a case by case basis.

Exome sequence genotype imputation in globally diverse hexaploid wheat accessions.

KEY MESSAGE: Imputing genotypes from the 90K SNP chip to exome sequence in wheat was moderately accurate. We investigated the factors that affect imputation and propose several strategies to improve accuracy. Imputing genetic marker genotypes from low to high density has been proposed as a cost-effective strategy to increase the power of downstream analyses (e.g. genome-wide association studies and genomic prediction) for a given budget. However, imputation is often imperfect and its accuracy depends on several factors. Here, we investigate the effects of reference population selection algorithms, marker density and imputation algorithms (Beagle4 and FImpute) on the accuracy of imputation from low SNP density (9K array) to the Infinium 90K single-nucleotide polymorphism (SNP) array for a collection of 837 hexaploid wheat Watkins landrace accessions. Based on these results, we then used the best performing reference selection and imputation algorithms to investigate imputation from 90K to exome sequence for a collection of 246 globally diverse wheat accessions. Accession-to-nearest-entry and genomic relationship-based methods were the best performing selection algorithms, and FImpute resulted in higher accuracy and was more efficient than Beagle4. The accuracy of imputing exome capture SNPs was comparable to imputing from 9 to 90K at approximately 0.71. This relatively low imputation accuracy is in part due to inconsistency between 90K and exome sequence formats. We also found the accuracy of imputation could be substantially improved to 0.82 when choosing an equivalent number of exome SNP, instead of 90K SNPs on the existing array, as the lower density set. We present a number of recommendations to increase the accuracy of exome imputation.