In silico analysis enabling informed design for genome editing in medicinal cannabis; gene families and variant characterisation.

BACKGROUND: Cannabis has been used worldwide for centuries for industrial, recreational and medicinal use, however, to date no successful attempts at editing genes involved in cannabinoid biosynthesis have been reported. This study proposes and develops an in silico best practices approach for the design and implementation of genome editing technologies in cannabis to target all genes involved in cannabinoid biosynthesis. RESULTS: A large dataset of reference genomes was accessed and mined to determine copy number variation and associated SNP variants for optimum target edit sites for genotype independent editing. Copy number variance and highly polymorphic gene sequences exist in the genome making genome editing using CRISPR, Zinc Fingers and TALENs technically difficult. Evaluation of allele or additional gene copies was determined through nucleotide and amino acid alignments with comparative sequence analysis performed. From determined gene copy number and presence of SNPs, multiple online CRISPR design tools were used to design sgRNA targeting every gene, accompanying allele and homologs throughout all involved pathways to create knockouts for further investigation. Universal sgRNA were designed for highly homologous sequences using MultiTargeter and visualised using Sequencher, creating unique sgRNA avoiding SNP and shared nucleotide locations targeting optimal edit sites. CONCLUSIONS: Using this framework, the approach has wider applications to all plant species regardless of ploidy number or highly homologous gene sequences. SIGNIFICANCE STATEMENT: Using this framework, a best-practice approach to genome editing is possible in all plant species, including cannabis, delivering a comprehensive in silico evaluation of the cannabinoid pathway diversity from a large set of whole genome sequences. Identification of SNP variants across all genes could improve genome editing potentially leading to novel applications across multiple disciplines, including agriculture and medicine.

Genomic prediction of serum biomarkers of health in early lactation.

In this study, we estimated genetic parameters and genomic prediction accuracies of serum biomarkers of health in early-lactation dairy cows. A single serum sample was taken from 1,393 cows, located on 14 farms in southeastern Australia, within 30 d after calving. Sera were analyzed for biomarkers of energy balance (ß-hydroxybutyrate and fatty acids), macromineral status (Ca and Mg), protein nutritional status (urea and albumin), and immune status (globulins, albumin-to-globulin ratio, and haptoglobin). After editing, 47,162 SNP marker genotypes were used to estimate genomic heritabilities and breeding values (GEBV) for these traits in ASReml. Heritabilities were low for ß-hydroxybutyrate, fatty acids, Ca, Mg, and urea (0.09 ± 0.04, 0.18 ± 0.05, 0.07 ± 0.04, 0.19 ± 0.06, and 0.18 ± 0.05, respectively), and moderate for albumin, globulins, and albumin-to-globulin ratio (0.27 ± 0.06, 0.46 ± 0.06, and 0.41 ± 0.06, respectively). The heritability of haptoglobin concentration was close to 0. The magnitude of genetic correlations between traits (estimated using bivariate models) varied considerably (0.01 to 0.96), and standard errors of these correlations were high (0.02 to 0.44). Interestingly, the direction of most genetic correlations was favorable, suggesting that selecting for more optimal concentrations of one biomarker may result in more optimal concentrations of other biomarkers. Correlations between biomarker GEBV and existing breeding values for survival, somatic cell count, and daughter fertility were small to moderate (0.07 to 0.45) and favorable, whereas correlations with breeding values for milk production traits were small (≤0.15). Accuracies of GEBV were evaluated by using 5-fold cross validation, and by calculating accuracies from prediction error variances associated with the GEBV. Accuracies of GEBV predicted using 5-fold cross validation were low (0.05 to 0.27), whereas the means of individual accuracies were greater, ranging from 0.31 to 0.51. Although increasing the size of the reference population should theoretically improve accuracies, our results suggest that genomic prediction of health biomarkers may allow identification of cows that are less susceptible to diseases in early lactation.

Metabolic profiling of early-lactation dairy cows using milk mid-infrared spectra.

Metabolic disorders in early lactation have negative effects on dairy cow health and farm profitability. One method for monitoring the metabolic status of cows is metabolic profiling, which uses associations between the concentrations of several metabolites in serum and the presence of metabolic disorders. In this cross-sectional study, we investigated the use of mid-infrared (MIR) spectroscopy of milk for predicting the concentrations of these metabolites in serum. Between July and October 2017, serum samples were taken from 773 early-lactation Holstein Friesian cows located on 4 farms in the Gippsland region of southeastern Victoria, Australia, on the same day as milk recording. The concentrations in sera of ß-hydroxybutyrate (BHB), fatty acids, urea, Ca, Mg, albumin, and globulins were measured by a commercial diagnostic laboratory. Optimal concentration ranges for each of the 7 metabolites were obtained from the literature. Animals were classified as being either affected or unaffected with metabolic disturbances based on these ranges. Milk samples were analyzed by MIR spectroscopy. The relationships between serum metabolite concentrations and MIR spectra were investigated using partial least squares regression. Partial least squares discriminant analyses (PLS-DA) were used to classify animals as being affected or not affected with metabolic disorders. Calibration equations were constructed using data from a randomly selected subset of cows (n = 579). Data from the remaining cows (n = 194) were used for validation. The coefficient of determination (R2) of serum BHB, fatty acids, and urea predictions were 0.48, 0.61, and 0.90, respectively. Predictions of Ca, Mg, albumin, and globulin concentrations were poor (0.06 ≤ R2 ≤ 0.17). The PLS-DA models could predict elevated fatty acid and urea concentrations with an accuracy of approximately 77 and 94%, respectively. A second independent validation data set was assembled in March 2018, comprising blood and milk samples taken from 105 autumn-calving cows of various breeds. The accuracies of BHB and fatty acid predictions were similar to those obtained using the first validation data set. The PLS-DA results were difficult to interpret due to the low prevalence of metabolic disorders in the data set. Our results demonstrate that MIR spectroscopy of milk shows promise for predicting the concentration of BHB, fatty acids, and urea in serum; however, more data are needed to improve prediction accuracies.

Adaptation responses in milk fat yield and methane emissions of dairy cows when wheat was included in their diet for 16 weeks.

Short-term studies have shown that feeding dairy cows diets containing a high proportion (>40%) of wheat may result in reduced milk fat concentration and reduced CH4 emissions (g of CH4/cow per d), but no long-term studies have been done on these responses. This study compared the milk production and CH4 responses when 24 dairy cows were fed diets containing high proportions of either wheat or corn over 16 wk. Cows were assigned to 2 groups and offered a diet (CRN) containing 10.0 kg of dry matter/d of crushed corn grain, 1.8 kg of dry matter/d of canola meal, 0.2 kg of dry matter/d of minerals, and 11.0 kg of dry matter/d of chopped alfalfa hay or a similar diet (WHT) in which wheat replaced the corn. Dry matter intake and milk yields of individual cows were measured daily. Methane emissions from individual cows were measured using controlled climate respiration chambers over 2 consecutive days during each of wk 4, 10, and 16. Milk composition was measured on the 2 d when cows were in chambers during wk 4, 10, and 16. Over the 16-wk experimental period, total dry matter intake remained relatively constant and similar for the 2 dietary treatment groups. At wk 4, CH4 emission, CH4 yield (g of CH4/kg of dry matter intake), milk fat yield, and milk fat concentration were substantially less in cows fed the WHT diet compared with the same metrics in cows fed the CRN diet; but these differences were not apparent at wk 10 and 16. The responses over time in these metrics were not similar in all cows. In 4 cows fed the WHT diet, CH4 yield, milk fat concentration, and milk fat yield remained relatively constant from wk 4 to 16, whereas for 5 fed the WHT diet, their CH4 emissions, milk fat yields, and milk fat concentrations almost doubled between wk 4 and 16. In the short term (4 wk), the inclusion of approximately 45% wheat instead of corn in the diet of cows resulted in reductions of 39% in CH4 yield, 35% in milk fat concentration, and 40% in milk fat yield. However, these reductions did not persist to wk 10 or beyond. Our data indicate that cows do not all respond in the same way with some "adaptive" cows showing a marked increase in CH4 yield, milk fat concentration, and milk fat yield after wk 4, whereas in other "nonadaptive" cows, these metrics were persistently inhibited to 16 wk. This research shows that short-term studies on dietary interventions to mitigate enteric CH4 emissions may not always predict the long-term effects of such interventions.

Accelerating wheat breeding for end-use quality with multi-trait genomic predictions incorporating near infrared and nuclear magnetic resonance-derived phenotypes.

KEY MESSAGE: Using NIR and NMR predictions of quality traits overcomes a major barrier for the application of genomic selection to accelerate improvement in grain end-use quality traits of wheat. Grain end-use quality traits are among the most important in wheat breeding. These traits are difficult to breed for, as their assays require flour quantities only obtainable late in the breeding cycle, and are expensive. These traits are therefore an ideal target for genomic selection. However, large reference populations are required for accurate genomic predictions, which are challenging to assemble for these traits for the same reasons they are challenging to breed for. Here, we use predictions of end-use quality derived from near infrared (NIR) or nuclear magnetic resonance (NMR), that require very small amounts of flour, as well as end-use quality measured by industry standard assay in a subset of accessions, in a multi-trait approach for genomic prediction. The NIR and NMR predictions were derived for 19 end-use quality traits in 398 accessions, and were then assayed in 2420 diverse wheat accessions. The accessions were grown out in multiple locations and multiple years, and were genotyped for 51208 SNP. Incorporating NIR and NMR phenotypes in the multi-trait approach increased the accuracy of genomic prediction for most quality traits. The accuracy ranged from 0 to 0.47 before the addition of the NIR/NMR data, while after these data were added, it ranged from 0 to 0.69. Genomic predictions were reasonably robust across locations and years for most traits. Using NIR and NMR predictions of quality traits overcomes a major barrier for the application of genomic selection for grain end-use quality traits in wheat breeding.

Heat Stress in Dairy Cattle Alters Lipid Composition of Milk.

Heat stress, potentially affecting both the health of animals and the yield and composition of milk, occurs frequently in tropical, sub-tropical and temperate regions. A simulated acute heat stress experiment was conducted in controlled-climate chambers and milk samples collected before, during and after the heat challenge. Milk lipid composition, surveyed using LC-MS, showed significant changes in triacylglycerol (TAG) and polar lipid profiles. Heat stress (temperature-humidity index up to 84) was associated with a reduction in TAG groups containing short- and medium-chain fatty acids and a concomitant increase in those containing long-chain fatty acids. The abundance of five polar lipid classes including phosphatidylethanolamine, phosphatidylserine, phosphatidylcholine, lysophosphatidylcholine and glucosylceramide, was found to be significantly reduced during heat stress. Lysophosphatidylcholine, showing the greatest reduction in concentration, also displayed a differential response between heat tolerant and heat susceptible cows during heat stress. This phospholipid could be used as a heat stress biomarker for dairy cattle. Changes in TAG profile caused by heat stress are expected to modify the physical properties of milk fat, whereas the reduction of phospholipids may affect the nutritional value of milk. The results are discussed in relation to animal metabolism adaptation in the event of acute heat stress.

Molecular breeding of transgenic white clover (Trifolium repens L.) with field resistance to Alfalfa mosaic virus through the expression of its coat protein gene.

Viral diseases, such as Alfalfa mosaic virus (AMV), cause significant reductions in the productivity and vegetative persistence of white clover plants in the field. Transgenic white clover plants ectopically expressing the viral coat protein gene encoded by the sub-genomic RNA4 of AMV were generated. Lines carrying a single copy of the transgene were analysed at the molecular, biochemical and phenotypic level under glasshouse and field conditions. Field resistance to AMV infection, as well as mitotic and meiotic stability of the transgene, were confirmed by phenotypic evaluation of the transgenic plants at two sites within Australia. The T(0) and T(1) generations of transgenic plants showed immunity to infection by AMV under glasshouse and field conditions, while the T(4) generation in an agronomically elite 'Grasslands Sustain' genetic background, showed a very high level of resistance to AMV in the field. An extensive biochemical study of the T(4) generation of transgenic plants, aiming to evaluate the level and composition of natural toxicants and key nutritional parameters, showed that the composition of the transgenic plants was within the range of variation seen in non-transgenic populations.