Epichloë seed transmission efficiency is influenced by plant defense response mechanisms.

Asexual Epichloë are endophytic fungi that form mutualistic symbioses with cool-season grasses, conferring to their hosts protection against biotic and abiotic stresses. Symbioses are maintained between grass generations as hyphae are vertically transmitted from parent to progeny plants through seed. However, endophyte transmission to the seed is an imperfect process where not all seeds become infected. The mechanisms underpinning the varying efficiencies of seed transmission are poorly understood. Host gene expression in response to Epichloë sp. LpTG-3 strain AR37 was examined within inflorescence primordia and ovaries of high and low endophyte transmission genotypes within a single population of perennial ryegrass. A genome-wide association study was conducted to identify population-level single nucleotide polymorphisms (SNPs) and associated genes correlated with vertical transmission efficiency. For low transmitters of AR37, upregulation of perennial ryegrass receptor-like kinases and resistance genes, typically associated with phytopathogen detection, comprised the largest group of differentially expressed genes (DEGs) in both inflorescence primordia and ovaries. DEGs involved in signaling and plant defense responses, such as cell wall modification, secondary metabolism, and reactive oxygen activities were also abundant. Transmission-associated SNPs were associated with genes for which gene ontology analysis identified "response to fungus" as the most significantly enriched term. Moreover, endophyte biomass as measured by quantitative PCR of Epichloë non-ribosomal peptide synthetase genes, was significantly lower in reproductive tissues of low-transmission hosts compared to high-transmission hosts. Endophyte seed-transmission efficiency appears to be influenced primarily by plant defense responses which reduce endophyte colonization of host reproductive tissues.

Genomic Prediction of Complex Traits in Forage Plants Species: Perennial Grasses Case.

The majority of forage grass species are obligate outbreeders. Their breeding classically consists of an initial selection on spaced plants for highly heritable traits such as disease resistances and heading date, followed by familial selection on swards for forage yield and quality traits. The high level of diversity and heterozygosity, and associated decay of linkage disequilibrium (LD) over very short genomic distances, has hampered the implementation of genomic selection (GS) in these species. However, next generation sequencing technologies in combination with the development of genomic resources have recently facilitated implementation of GS in forage grass species such as perennial ryegrass (Lolium perenne L.), switchgrass (Panicum virgatum L.), and timothy (Phleum pratense L.). Experimental work and simulations have shown that GS can increase significantly the genetic gain per unit of time for traits with different levels of heritability. The main reasons are (1) the possibility to select single plants based on their genomic estimated breeding values (GEBV) for traits measured at sward level, (2) a reduction in the duration of selection cycles, and less importantly (3) an increase in the selection intensity associated with an increase in the genetic variance used for selection. Nevertheless, several factors should be taken into account for the successful implementation of GS in forage grasses. For example, it has been shown that the level of relatedness between the training and the selection population is particularly critical when working with highly structured meta-populations consisting of several genetic groups. A sufficient number of markers should be used to estimate properly the kinship between individuals and to reflect the variability of major QTLs. It is also important that the prediction models are trained for relevant environments when dealing with traits with high genotype × environment interaction (G × E). Finally, in these outbreeding species, measures to reduce inbreeding should be used to counterbalance the high selection intensity that can be achieved in GS.

Outlier analyses and genome-wide association study identify glgC and ERD6-like 4 as candidate genes for foliar water-soluble carbohydrate accumulation in Trifolium repens.

Increasing water-soluble carbohydrate (WSC) content in white clover is important for improving nutritional quality and reducing environmental impacts from pastoral agriculture. Elucidation of genes responsible for foliar WSC variation would enhance genetic improvement by enabling molecular breeding approaches. The aim of the present study was to identify single nucleotide polymorphisms (SNPs) associated with variation in foliar WSC in white clover. A set of 935 white clover individuals, randomly sampled from five breeding pools selectively bred for divergent (low or high) WSC content, were assessed with 14,743 genotyping-by-sequencing SNPs, using three outlier detection methods: PCAdapt, BayeScan and KGD-FST. These analyses identified 33 SNPs as discriminating between high and low WSC populations and putatively under selection. One SNP was located in the intron of ERD6-like 4, a gene coding for a sugar transporter located on the vacuole membrane. A genome-wide association study using a subset of 605 white clover individuals and 5,757 SNPs, identified a further 12 SNPs, one of which was associated with a starch biosynthesis gene, glucose-1-phosphate adenylyltransferase, glgC. Our results provide insight into genomic regions underlying WSC accumulation in white clover, identify candidate genomic regions for further functional validation studies, and reveal valuable information for marker-assisted or genomic selection in white clover.

Deterministic and stochastic modelling of impacts from genomic selection and phenomics on genetic gain for perennial ryegrass dry matter yield.

Increasing the efficiency of current forage breeding programs through adoption of new technologies, such as genomic selection (GS) and phenomics (Ph), is challenging without proof of concept demonstrating cost effective genetic gain (∆G). This paper uses decision support software DeltaGen (tactical tool) and QU-GENE (strategic tool), to model and assess relative efficiency of five breeding methods. The effect on ∆G and cost ($) of integrating GS and Ph into an among half-sib (HS) family phenotypic selection breeding strategy was investigated. Deterministic and stochastic modelling were conducted using mock data sets of 200 and 1000 perennial ryegrass HS families using year-by-season-by-location dry matter (DM) yield data and in silico generated data, respectively. Results demonstrated short (deterministic)- and long-term (stochastic) impacts of breeding strategy and integration of key technologies, GS and Ph, on ∆G. These technologies offer substantial improvements in the rate of ∆G, and in some cases improved cost-efficiency. Applying 1% within HS family GS, predicted a 6.35 and 8.10% ∆G per cycle for DM yield from the 200 HS and 1000 HS, respectively. The application of GS in both among and within HS selection provided a significant boost to total annual ∆G, even at low GS accuracy rA of 0.12. Despite some reduction in ∆G, using Ph to assess seasonal DM yield clearly demonstrated its impact by reducing cost per percentage ∆G relative to standard DM cuts. Open-source software tools, DeltaGen and QuLinePlus/QU-GENE, offer ways to model the impact of breeding methodology and technology integration under a range of breeding scenarios.

Multi-Trait Genomic Prediction Improves Predictive Ability for Dry Matter Yield and Water-Soluble Carbohydrates in Perennial Ryegrass.

In perennial ryegrass (Lolium perenne L), annual and seasonal dry matter yield (DMY) and nutritive quality of herbage are high-priority traits targeted for improvement through selective breeding. Genomic prediction (GP) has proven to be a valuable tool for improving complex traits and may be further enhanced through the use of multi-trait (MT) prediction models. In this study, we evaluated the relative performance of MT prediction models to improve predictive ability for DMY and key nutritive quality traits, using two different training populations (TP1, n = 463 and TP2, n = 517) phenotyped at multiple locations. MT models outperformed single-trait (ST) models by 24% to 59% for DMY and 67% to 105% for nutritive quality traits, such as low, high, and total WSC, when a correlated secondary trait was included in both the training and test set (MT-CV2) or in the test set alone (MT-CV3) (trait-assisted genomic selection). However, when a secondary trait was included in training set and not the test set (MT-CV1), the predictive ability was not statistically significant (p > 0.05) compared to the ST model. We evaluated the impact of training set size when using a MT-CV2 model. Using a highly correlated trait (rg = 0.88) as the secondary trait in the MT-CV2 model, there was no loss in predictive ability for DMY even when the training set was reduced to 50% of its original size. In contrast, using a weakly correlated secondary trait (rg = 0.56) in the MT-CV2 model, predictive ability began to decline when the training set size was reduced by only 11% from its original size. Using a ST model, genomic predictive ability in a population unrelated to the training set was poor (rp = -0.06). However, when using an MT-CV2 model, the predictive ability was positive and high (rp = 0.76) for the same population. Our results demonstrate the first assessment of MT models in forage species and illustrate the prospects of using MT genomic selection in forages, and other outcrossing plant species, to accelerate genetic gains for complex agronomical traits, such as DMY and nutritive quality characteristics.

Genomic Predictive Ability for Foliar Nutritive Traits in Perennial Ryegrass.

Forage nutritive value impacts animal nutrition, which underpins livestock productivity, reproduction and health. Genetic improvement for nutritive traits in perennial ryegrass has been limited, as they are typically expensive and time-consuming to measure through conventional methods. Genomic selection is appropriate for such complex and expensive traits, enabling cost-effective prediction of breeding values using genome-wide markers. The aims of the present study were to assess the potential of genomic selection for a range of nutritive traits in a multi-population training set, and to quantify contributions of family, location and family-by-location variance components to trait variation and heritability for nutritive traits. The training set consisted of a total of 517 half-sibling (half-sib) families, from five advanced breeding populations, evaluated in two distinct New Zealand grazing environments. Autumn-harvested samples were analyzed for 18 nutritive traits and maternal parents of the half-sib families were genotyped using genotyping-by-sequencing. Significant (P < 0.05) family variance was detected for all nutritive traits and genomic heritability (h2g ) was moderate to high (0.20 to 0.74). Family-by-location interactions were significant and particularly large for water soluble carbohydrate (WSC), crude fat, phosphorus (P) and crude protein. GBLUP, KGD-GBLUP and BayesCπ genomic prediction models displayed similar predictive ability, estimated by 10-fold cross validation, for all nutritive traits with values ranging from r = 0.16 to 0.45 using phenotypes from across two locations. High predictive ability was observed for the mineral traits sulfur (0.44), sodium (0.45) and magnesium (0.45) and the lowest values were observed for P (0.16), digestibility (0.22) and high molecular weight WSC (0.23). Predictive ability estimates for most nutritive traits were retained when marker number was reduced from one million to as few as 50,000. The moderate to high predictive abilities observed suggests implementation of genomic selection is feasible for most of the nutritive traits examined.

A large-scale metabolomics study to harness chemical diversity and explore biochemical mechanisms in ryegrass.

Perennial ryegrass (Lolium perenne) is integral to temperate pastoral agriculture, which contributes most of the milk and meat production worldwide. Chemical profiles and diversity of ryegrass offer several opportunities to harness specific traits and elucidate underlying biological mechanisms for forage improvement. We conducted a large-scale metabolomics study of perennial ryegrass comprising 715 genotypes, representing 118 populations from 21 countries. Liquid/gas chromatography-mass spectrometry based targeted and non-targeted techniques were used to analyse fructan oligosaccharides, lipids, fatty acid methyl esters, polar and semi-polar compounds. Fructan diversity across all genotypes was evaluated, high- and low-sugar groups identified, and fructan accumulation mechanisms explored. Metabolites differentiating the two groups were characterised, modules and pathways they represent deduced, and finally, visualisation and interpretation provided in a biological context. We also demonstrate a workflow for large-scale metabolomics studies from raw data through to statistical and pathway analysis. Raw files and metadata are available at the MetaboLights database.

Seed Transmission of Epichloë Endophytes in Lolium perenne Is Heavily Influenced by Host Genetics.

Vertical transmission of symbiotic Epichloë endophytes from host grasses into progeny seed is the primary mechanism by which the next generation of plants is colonized. This process is often imperfect, resulting in endophyte-free seedlings which may have poor ecological fitness if the endophyte confers protective benefits to its host. In this study, we investigated the influence of host genetics and environment on the vertical transmission of Epichloë festucae var. lolii strain AR37 in the temperate forage grass Lolium perenne. The efficiency of AR37 transmission into the seed of over 500 plant genotypes from five genetically diverse breeding populations was determined. In Populations I-III, which had undergone previous selection for high seed infection by AR37, mean transmission was 88, 93, and 92%, respectively. However, in Populations IV and V, which had not undergone previous selection, mean transmission was 69 and 70%, respectively. The transmission values, together with single-nucleotide polymorphism data obtained using genotyping-by-sequencing for each host, was used to develop a genomic prediction model for AR37 seed transmission. The predictive ability of the model was estimated at r = 0.54. While host genotype contributed greatly to differences in AR37 seed transmission, undefined environmental variables also contributed significantly to seed transmission across different years and geographic locations. There was evidence for a small host genotype-by-environment effect; however this was less pronounced than genotype or environment alone. Analysis of endophyte infection levels in parent plants within Populations I and IV revealed a loss of endophyte infection over time in Population IV only. This population also had lower average tiller infection frequencies than Population I, suggesting that AR37 failed to colonize all the daughter tillers and therefore seeds. However, we also observed that infection of seed by AR37 may fail during or after initiation of floral development from plants where all tillers remained endophyte-infected over time. While the effects of environment and host genotype on fungal endophyte transmission have been evaluated previously, this is the first study that quantifies the relative impacts of host genetics and environment on endophyte vertical transmission.

Protocol: a versatile, inexpensive, high-throughput plant genomic DNA extraction method suitable for genotyping-by-sequencing.

BACKGROUND: The recent development of next-generation sequencing DNA marker technologies, such as genotyping-by-sequencing (GBS), generates thousands of informative single nucleotide polymorphism markers in almost any species, regardless of genomic resources. This enables poorly resourced or "orphan" crops/species access to high-density, high-throughput marker platforms which have revolutionised population genetics studies and plant breeding. DNA quality underpins success of GBS methods as the DNA must be amenable to restriction enzyme digestion and sequencing. A barrier to implementing GBS technologies is access to inexpensive, high-throughput extraction methods that yield sequencing-quality genomic DNA (gDNA) from plants. Several high-throughput DNA extraction methods are available, but typically provide low yield or poor quality gDNA, or are costly (US$6-$9/sample) for consumables. RESULTS: We modified a non-organic solvent protocol to extract microgram quantities (1-13 µg) of sequencing-quality high molecular weight gDNA inexpensively in 96-well plates from either fresh, freeze-dried or silica gel-dried plant tissue. The protocol was effective for several easy and difficult-to-extract forage, crop, horticultural and common model species including Trifolium, Medicago, Lolium, Secale, Festuca, Malus, Oryza, and Arabidopsis. The extracted DNA was of high molecular weight and digested readily with restriction enzymes. Contrasting with other extraction protocols we assessed, Illumina-based sequencing of GBS libraries developed from this gDNA had very uniform high quality base-calls to the end of sequence reads. Furthermore, DNA extracted using this method has been sequenced successfully with the PacBio long-read platform. The protocol is scalable, readily automated without requirement for fume hoods, requires approximately three hours to process 192 samples (384-576 samples/day), and is inexpensive at US$0.62/sample for consumables. CONCLUSIONS: This versatile, scalable and simple protocol yields high molecular weight genomic DNA suitable for restriction enzyme digestion and next-generation sequencing applications including GBS and long-read sequencing platforms such as PacBio. The low cost, high-throughput, and extraction of high quality gDNA from a range of fresh and dried source plant material makes this method suitable for many sequencing and genotyping applications including large-scale sample screening underpinning breeding programmes.

Low pyrrolizidine alkaloid levels in perennial ryegrass is associated with the absence of a homospermidine synthase gene.

BACKGROUND: Pyrrolizidine alkaloids (PAs) are a class of secondary metabolites that function as feeding deterrents in a range of different plant species. In perennial ryegrass (Lolium perenne L.) the only PAs that have been identified are the thesinine-rhamnoside group, which displays significant genetic variation. Homospermidine synthase (HSS) has evolved from deoxyhypusine synthase (DHS) and catalyses the first step in the PA pathway, making it a key candidate for the investigation of genes influencing observed PA trait variation. RESULTS: During PCR amplification and sequence analysis of DHS we identified two putative HSS genes in perennial ryegrass. One of the genes (LpHSS1) was absent in some perennial ryegrass plants. Thesinine-rhamnoside levels were measured using liquid chromatography coupled with mass spectrometry in a diverse association mapping population, consisting of 693 plants free of fungal endophytic symbionts. Association tests that accounted for population structure identified a significant association of absence of the LpHSS1 gene with lower levels of thesinine-rhamnoside PAs. HSS-like gene sequences were identified for other grass species of the Poaceae, including tall fescue, wheat, maize and sorghum. CONCLUSION: HSS is situated at the crucial first step in the PA pathway making it an important candidate gene for investigation of involvement in PA phenotypic variation. In this study, PA level in perennial ryegrass was strongly associated with the presence or absence of the LpHSS1 gene. A genetic marker, developed for the presence/absence of LpHSS1, may be used for marker-assisted breeding to either lower or increase PAs in breeding populations of perennial or Italian ryegrass to investigate a potential role in the deterrence of herbivore pests. The presence of HSS-like genes in several other Poaceae species suggests that PA biosynthesis may occur in plant family members beyond perennial ryegrass and tall fescue and identifies a potential route for manipulating PA levels.

Predictive ability of genomic selection models in a multi-population perennial ryegrass training set using genotyping-by-sequencing.

KEY MESSAGE: Genomic prediction models for multi-year dry matter yield, via genotyping-by-sequencing in a composite training set, demonstrate potential for genetic gain improvement through within-half sibling family selection. Perennial ryegrass (Lolium perenne L.) is a key source of nutrition for ruminant livestock in temperate environments worldwide. Higher seasonal and annual yield of herbage dry matter (DMY) is a principal breeding objective but the historical realised rate of genetic gain for DMY is modest. Genomic selection was investigated as a tool to enhance the rate of genetic gain. Genotyping-by-sequencing (GBS) was undertaken in a multi-population (MP) training set of five populations, phenotyped as half-sibling (HS) families in five environments over 2 years for mean herbage accumulation (HA), a measure of DMY potential. GBS using the ApeKI enzyme yielded 1.02 million single-nucleotide polymorphism (SNP) markers from a training set of n = 517. MP-based genomic prediction models for HA were effective in all five populations, cross-validation-predictive ability (PA) ranging from 0.07 to 0.43, by trait and target population, and 0.40-0.52 for days-to-heading. Best linear unbiased predictor (BLUP)-based prediction methods, including GBLUP with either a standard or a recently developed (KGD) relatedness estimation, were marginally superior or equal to ridge regression and random forest computational approaches. PA was principally an outcome of SNP modelling genetic relationships between training and validation sets, which may limit application for long-term genomic selection, due to PA decay. However, simulation using data from the training experiment indicated a twofold increase in genetic gain for HA, when applying a prediction model with moderate PA in a single selection cycle, by combining among-HS family selection, based on phenotype, with within-HS family selection using genomic prediction.

Untargeted Metabotyping Lolium perenne Reveals Population-Level Variation in Plant Flavonoids and Alkaloids.

Metabolomics provides a powerful platform to characterize plants at the biochemical level, allowing a search for underlying genes and associations with higher level complex traits such as yield and nutritional value. Efficient and reliable methods to characterize metabolic variation in economically important species are considered of high value to the evaluation and prioritization of germplasm and breeding lines. In this investigation, a large-scale metabolomic survey was performed on a collection of diverse perennial ryegrass (Lolium perenne L.) plants. A total of 2,708 data files, derived from liquid chromatography coupled to high resolution mass spectrometry (LCMS), were selected to assess the effectiveness and efficiency of applying high throughput metabolomics to survey chemical diversity in plant populations. The data set was generated from 23 ryegrass populations, with 3-25 genotypes per population, and five clonal replicates per genotype. We demonstrate an integrated approach to rapidly mine and analyze metabolic variation from this large, multi-batch LCMS data set. After performing quality control, statistical data mining and peak annotation, a wide range of variation for flavonoid glycosides and plant alkaloids was discovered among the populations. Structural variation of flavonoids occurs both in aglycone structures and acetylated/malonylated/feruloylated sugar moieties. The discovery of comprehensive metabolic variation among the plant populations offers opportunities to probe into the genetic basis of the variation, and provides a valuable resource to gain insight into biochemical functions and to relate metabolic variation with higher level traits in the species.

A QTL analysis of host plant effects on fungal endophyte biomass and alkaloid expression in perennial ryegrass.

The association between perennial ryegrass (Loliumperenne L.) and its Epichloë fungal endophyte symbiont, Epichloëfestucae var. lolii, supports the persistence of ryegrass-based pastures principally by producing bioactive alkaloid compounds that deter invertebrate herbivory. The host plant genotype affects endophyte trait expression, and elucidation of the underlying genetic mechanisms would enhance understanding of the symbiosis and support improvement of inplanta endophyte performance through plant breeding. Rapid metabolite profiling and enzyme-linked immunosorbent assay were used to quantify endophyte alkaloids and mycelial mass (MM) in leaves harvested, in consecutive autumns, from an F1 mapping population hosting standard toxic endophyte. Co-aligned quantitative trait loci (QTL) on linkage groups (LG)2, LG4 and LG7 for MM and concentrations of alkaloids peramine and ergovaline confirmed host plant effects on both MM and alkaloid level and inferred the effect on alkaloids was modulated through the quantity of endophyte present in the leaf tissue. For ergovaline, host regulation independent of endophyte concentration was also indicated, by the presence of MM-independent ergovaline QTL on LG4 and LG7. Partitioning of host genetic influence between MM-dependent and MM-independent mechanisms was also observed for the alkaloid N-formylloline (NFL), in a second mapping population harbouring a tall fescue-sourced endophyte. Single-marker analysis on repeated MM and NFL measures identified marker-trait associations at nine genome locations, four affecting both NFL and MM but five influencing NFL concentration alone. Co-occurrence of QTL on LG3, LG4 and LG7 in both mapping populations is evidence for host regulatory loci effective across genetic backgrounds and independent of endophyte variant. Variation at these loci may be exploited using marker-assisted breeding to improve endophyte trait expression in different host population × endophyte combinations.

Plant vigour at establishment and following defoliation are both associated with responses to drought in perennial ryegrass (Lolium perenne L.).

Periodic drought events present a significant and, with climate change, increasing constraint on temperate forage plants' production. Consequently, improving plants' adaptive response to abiotic stress is a key goal to ensure agricultural productivity in these regions. In this study we developed a new methodology, using both area-based comparison and soil water content measurements of individual non-irrigated and irrigated clones, to assess performance of perennial ryegrass (Lolium perenne L.) genotypes subjected to moisture stress in a simulated competitive environment. We applied this method to the evaluation of a full-sibling population from a pair cross between genotypes from a New Zealand cultivar and a Moroccan ecotype. Our hypothesis was that: (i) both leaf lamina regrowth after defoliation (LR) and plant vigour affect plant performance during drought and rehydration; and (ii) quantitative trait loci (QTLs) associated with plant performance under moisture stress could be identified. Differences amongst genotypes in dry matter (DM) production, early vigour at establishment, leaf elongation rate and LR were measured. LR explained most of the variation in DM production during exposure to moisture deficit and rehydration followed by plant vigour, indicated by initial DM production in both treatments and subsequent measures of DM production of irrigated clones. We identified two main QTL regions associated with DM production and LR, both during drought exposure and rehydration. Further research focused on these regions should improve our understanding of the genetic control of drought response in this forage crop and potentially other grass species with significant synteny, and support improvement in performance through molecular breeding approaches.

Mutualistic fungal endophytes in the Triticeae - survey and description.

Grasses of the tribe Triticeae were screened to determine the presence of mutualistic epichloae fungal endophytes. Over 1500 accessions, from more than 250 species, encompassing 22 genera within the Triticeae were screened using immunodetection and direct staining/microscopy techniques. Only two genera, Elymus and Hordeum, were identified as harbouring epichloae endophytes with accessions native to a range of countries including Canada, China, Iran, Kazakhstan, Kyrgyzstan, Mongolia, Russia and the USA. Genetic analysis based on simple sequence repeat data revealed that the majority of endophytes cluster according to geographical regions rather than to host species; many strains isolated from Hordeum grouped with those derived from Elymus, and amongst the Elymus-derived strains, there was no clear correspondence between clustering topology and host species. This is the first detailed survey demonstrating the genetic diversity of epichloae endophytes within the Triticeae and highlights the importance of germplasm centres for not only preserving the genetic diversity of plant species but also the beneficial microorganisms they may contain.

Leaf Rubisco turnover in a perennial ryegrass (Lolium perenne L.) mapping population: genetic variation, identification of associated QTL, and correlation with plant morphology and yield.

This study tested the hypotheses that: (i) genetic variation in Rubisco turnover may exist in perennial ryegrass (Lolium perenne L.); (ii) such variation might affect nitrogen use efficiency and plant yield; and (iii) genetic control of Rubisco turnover might be amenable to identification by quantitative trait loci (QTL) mapping. A set of 135 full-sib F1 perennial ryegrass plants derived from a pair cross between genotypes from the cultivars 'Grasslands Impact' and 'Grasslands Samson' was studied to test these hypotheses. Leaf Rubisco concentration at different leaf ages was measured and modelled as a log-normal curve described by three mathematical parameters: D (peak Rubisco concentration), G (time of D), and F (curve standard deviation). Herbage dry matter (DM) yield and morphological traits (tiller weight (TW), tiller number (TN), leaf lamina length (LL), and an index of competitive ability (PI)) were also measured. The progeny exhibited continuous variation for all traits. Simple correlation and principal component analyses indicated that plant productivity was associated with peak Rubisco concentration and not Rubisco turnover. Lower DM was associated with higher leaf Rubisco concentration indicating that Rubisco turnover effects on plant productivity may relate to energy cost of Rubisco synthesis rather than photosynthetic capacity. QTL detection by a multiple QTL model identified seven significant QTL for Rubisco turnover and nine QTL for DM and morphological traits. An indication of the genetic interdependence of DM and the measures of Rubisco turnover was the support interval overlap involving QTL for D and QTL for TN on linkage group 5 in a cluster involving QTL for DM and PI. In this region, alleles associated with increased TN, DM, and PI were associated with decreased D, indicating that this region may regulate Rubisco concentration and plant productivity via increased tillering. A second cluster involving QTL for LL, TN, PI and DM was found on linkage group 2. The two clusters represent marker-trait associations that might be useful for marker-assisted plant breeding applications. In silico comparative analysis indicated conservation of the genetic loci controlling Rubisco concentration in perennial ryegrass and rice.

Semi-quantitative and structural metabolic phenotyping by direct infusion ion trap mass spectrometry and its application in genetical metabolomics.

The identification of quantitative trait loci (QTL) for plant metabolites requires the quantitation of these metabolites across a large range of progeny. We developed a rapid metabolic profiling method using both untargeted and targeted direct infusion tandem mass spectrometry (DIMSMS) with a linear ion trap mass spectrometer yielding sufficient precision and accuracy for the quantification of a large number of metabolites in a high-throughput environment. The untargeted DIMSMS method uses top-down data-dependent fragmentation yielding MS(2) and MS(3) spectra. We have developed software tools to assess the structural homogeneity of the MS(2) and MS(3) spectra hence their utility for phenotyping and genetical metabolomics. In addition we used a targeted DIMS(MS) method for rapid quantitation of specific compounds. This method was compared with targeted LC/MS/MS methods for these compounds. The DIMSMS methods showed sufficient precision and accuracy for QTL discovery. We phenotyped 200 individual Lolium perenne genotypes from a mapping population harvested in two consecutive years. Computational and statistical analyses identified 246 nominal m/z bins with sufficient precision and homogeneity for QTL discovery. Comparison of the data for specific metabolites obtained by DIMSMS with the results from targeted LC/MS/MS analysis showed that quantitation by this metabolic profiling method is reasonably accurate. Of the top 100 MS(1) bins, 22 ions gave one or more reproducible QTL across the 2 years.