An alfalfa MYB-like transcriptional factor MsMYBH positively regulates alfalfa seedling drought resistance and undergoes MsWAV3-mediated degradation.

Drought is a major threat to alfalfa (Medicago sativa L.) production. The discovery of important alfalfa genes regulating drought response will facilitate breeding for drought-resistant alfalfa cultivars. Here, we report a genome-wide association study of drought resistance in alfalfa. We identified and functionally characterized an MYB-like transcription factor gene (MsMYBH), which increases the drought resistance in alfalfa. Compared with the wild-types, the biomass and forage quality were enhanced in MsMYBH overexpressed plants. Combined RNA-seq, proteomics and chromatin immunoprecipitation analysis showed that MsMYBH can directly bind to the promoters of MsMCP1, MsMCP2, MsPRX1A and MsCARCAB to improve their expression. The outcomes of such interactions include better water balance, high photosynthetic efficiency and scavenge excess H2O2 in response to drought. Furthermore, an E3 ubiquitin ligase (MsWAV3) was found to induce MsMYBH degradation under long-term drought, via the 26S proteasome pathway. Furthermore, variable-number tandem repeats in MsMYBH promoter were characterized among a collection of germplasms, and the variation is associated with promoter activity. Collectively, our findings shed light on the functions of MsMYBH and provide a pivotal gene that could be leveraged for breeding drought-resistant alfalfa. This discovery also offers new insights into the mechanisms of drought resistance in alfalfa.

Non-targeted multimodal metabolomics data from ovine rumen fluid fractions.

From an animal health perspective, our understanding of the metabolites in rumen fluid across different host species is poorly understood. Here, we present a metabolomic data set generated using hydrophilic interaction liquid chromatography and semi-polar (C18) chromatography methods coupled to high-resolution mass spectrometry of fractionated ovine rumen samples.

Molecular insights into quality and authentication of sheep meat from proteomics and metabolomics.

Sheep meat (encompassing lamb, hogget and mutton) is an important source of animal protein in many countries, with a unique flavour and sensory profile compared to other red meats. Flavour, colour and texture are the key quality attributes contributing to consumer liking of sheep meat. Over the last decades, various factors from 'farm to fork', including production system (e.g., age, breed, feeding regimes, sex, pre-slaughter stress, and carcass suspension), post-mortem manipulation and processing (e.g., electrical stimulation, ageing, packaging types, and chilled and frozen storage) have been identified as influencing different aspects of sheep meat quality. However conventional meat-quality assessment tools are not able to elucidate the underlying mechanisms and pathways for quality variations. Advances in broad-based analytical techniques have offered opportunities to obtain deeper insights into the molecular changes of sheep meat which may become biomarkers for specific variations in quality traits and meat authenticity. This review provides an overview on how omics techniques, especially proteomics (including peptidomics) and metabolomics (including lipidomics and volatilomics) are applied to elucidate the variations in sheep meat quality, mainly in loin muscles, focusing on colour, texture and flavour, and as tools for authentication. SIGNIFICANCE: From this review, we observed that attempts have been made to utilise proteomics and metabolomics techniques on sheep meat products for elucidating pathways of quality variations due to various factors. For instance, the improvement of colour stability and tenderness could be associated with the changes to glycolysis, energy metabolism and endogenous antioxidant capacity. Several studies identify proteolysis as being important, but potentially conflicting for quality as the enhanced proteolysis improves tenderness and flavour, while reducing colour stability. The use of multiple analytical methods e.g., lipidomics, metabolomics, and volatilomics, detects a wider range of flavour precursors (including both water and lipid soluble compounds) that underlie the possible pathways for sheep meat flavour evolution. The technological advancement in omics (e.g., direct analysis-mass spectrometry) could make analysis of the proteins, lipids and metabolites in sheep meat routine, as well as enhance the confidence in quality determination and molecular-based assurance of meat authenticity.

Untargeted Multimodal Metabolomics Investigation of the Haemonchus contortus Exsheathment Secretome.

In nematodes that invade the gastro-intestinal tract of the ruminant, the process of larval exsheathment marks the transition from the free-living to the parasitic stages of these parasites. To investigate the secretome associated with larval exsheathment, a closed in vitro system that effectively reproduces the two basic components of an anaerobic rumen environment (CO2 and 39 °C) was developed to trigger exsheathment in one of the most pathogenic and model gastrointestinal parasitic nematodes, Haemonchus contortus (barber's pole worm). This study reports the use of multimodal untargeted metabolomics and lipidomics methodologies to identify the metabolic signatures and compounds secreted during in vitro larval exsheathment in the H. contortus infective third-stage larva (iL3). A combination of statistical and chemoinformatic analyses using three analytical platforms revealed a panel of metabolites detected post exsheathment and associated with amino acids, purines, as well as select organic compounds. The major lipid classes identified by the non-targeted lipidomics method applied were lysophosphatidylglycerols, diglycerides, fatty acyls, glycerophospholipids, and a triglyceride. The identified metabolites may serve as metabolic signatures to improve tractability of parasitic nematodes for characterizing small molecule host-parasite interactions related to pathogenesis, vaccine and drug design, as well as the discovery of metabolic biomarkers.

Genome and systems biology of Melilotus albus provides insights into coumarins biosynthesis.

Melilotus species are used as green manure and rotation crops worldwide and contain abundant pharmacologically active coumarins. However, there is a paucity of information on its genome and coumarin production and function. Here, we reported a chromosome-scale assembly of Melilotus albus genome with 1.04 Gb in eight chromosomes, containing 71.42% repetitive elements. Long terminal repeat retrotransposon bursts coincided with declining of population sizes during the Quaternary glaciation. Resequencing of 94 accessions enabled insights into genetic diversity, population structure, and introgression. Melilotus officinalis had relatively larger genetic diversity than that of M. albus. The introgression existed between M. officinalis group and M. albus group, and gene flows was from M. albus to M. officinalis. Selection sweep analysis identified candidate genes associated with flower colour and coumarin biosynthesis. Combining genomics, BSA, transcriptomics, metabolomics, and biochemistry, we identified a ß-glucosidase (BGLU) gene cluster contributing to coumarin biosynthesis. MaBGLU1 function was verified by overexpression in M. albus, heterologous expression in Escherichia coli, and substrate feeding, revealing its role in scopoletin (coumarin derivative) production and showing that nonsynonymous variation drives BGLU enzyme activity divergence in Melilotus. Our work will accelerate the understanding of biologically active coumarins and their biosynthetic pathways, and contribute to genomics-enabled Melilotus breeding.

Unveiling the Complexity of Red Clover (Trifolium pratense L.) Transcriptome and Transcriptional Regulation of Isoflavonoid Biosynthesis Using Integrated Long- and Short-Read RNAseq.

Red clover (Trifolium pratense L.) is used as forage and contains a high level of isoflavonoids. Although isoflavonoids in red clover were discovered a long time ago, the transcriptional regulation of isoflavonoid biosynthesis is virtually unknown because of the lack of accurate and comprehensive characterization of the transcriptome. Here, we used a combination of long-read (PacBio Iso-Seq) and short-read (Illumina) RNAseq sequencing to develop a more comprehensive full-length transcriptome in four tissues (root, stem, leaf, and flower) and to identify transcription factors possibly involved in isoflavonoid biosynthesis in red clover. Overall, we obtained 50,922 isoforms, including 19,860 known genes and 2817 novel isoforms based on the annotation of RefGen Tp_v2.0. We also found 1843 long non-coding RNAs, 1625 fusion genes, and 34,612 alternatively spliced events, with some transcript isoforms validated experimentally. A total of 16,734 differentially expressed genes were identified in the four tissues, including 43 isoflavonoid-biosynthesis-related genes, such as stem-specific expressed TpPAL, TpC4H, and Tp4CL and root-specific expressed TpCHS, TpCHI1, and TpIFS. Further, weighted gene co-expression network analysis and a targeted compound assay were combined to investigate the association between the isoflavonoid content and the transcription factors expression in the four tissues. Twelve transcription factors were identified as key genes for isoflavonoid biosynthesis. Among these transcription factors, the overexpression of TpMYB30 or TpRSM1-2 significantly increased the isoflavonoid content in tobacco. In particular, the glycitin was increased by 50-100 times in the plants overexpressing TpRSM1-2, in comparison to that in the WT plants. Our study provides a comprehensive and accurate annotation of the red clover transcriptome and candidate genes to improve isoflavonoid biosynthesis and accelerate research into molecular breeding in red clover or other crops.

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.

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.

Serum metabolomics using ultra performance liquid chromatography coupled to mass spectrometry in lactating dairy cows following a single dose of sporidesmin.

INTRODUCTION: Photosensitization is a common clinical sign in cows suffering from liver damage caused by the mycotoxin sporidesmin. This disease, called facial eczema (FE), is of major importance in New Zealand. Current techniques for diagnosing animals with subclinical sporidesmin-induced liver damage (i.e. without photosensitization) are nonspecific. In addition, little is known of the mechanisms involved in sporidesmin resistance, nor the early effects seen following low-dose sporidesmin intoxication. OBJECTIVE: The objective of this study was to identify individual metabolites or metabolic profiles that could be used as serum markers for early stage FE in lactating cows. METHODS: Results are presented from a 59-day sporidesmin challenge in Friesian-cross dairy cows. Serum metabolite profiles were obtained using reversed phase ultra-performance liquid chromatography (UPLC) electrospray ionization mass spectrometry (MS) and UPLC tandem MS. Multivariate and time series analyses were used to assess the data. RESULTS: Statistical analysis, both with and without the temporal component, could distinguish the profiles of animals with clinical signs from the others, but not those affected subclinically. An increase in the concentrations of a combination of taurine- and glycine-conjugated secondary bile acids (BAs) was the most likely cause of the separation. This is the first time that MS methods have been applied to FE and that bile acids changes have been detected in cattle exposed to sporidesmin. CONCLUSIONS: It is well known that BA concentrations increase during cholestasis due to damage to bile ducts and leakage of the bile. This is the first study to investigate metabolomic changes in serum following a sporidesmin challenge. Further work to establish the significance of the elevation of individual BAs concentrations in the serum of early-stage sporidesmin-poisoned cows is necessary.

Analysis of microRNA reveals cleistogamous and chasmogamous floret divergence in dimorphic plant.

Cleistogenes songorica, a grass species that exhibits two spatially different type of inflorescence, chastogamy (CH), flowers localized at the top, and cleistogamy (CL) flowers embedded in leaf sheath. This study aimed at dissecting reasons underlying these distinct floral development patterns at morphological and microRNA level. Phenotyping for CH and CL was conducted and four small RNA libraries were constructed from the CH and CL flowers for high-throughput sequencing to identify the differentiated miRNAs. As results, spikelet, stigma, anther, lemma and lodicule length of CH flowers were found larger than that of CL, and so was seed setting. Also, 17 flower-related differential expression miRNAs were identified which were associated with floral organ development and morphogenesis, and the flower development. Further results showed that miR159a.1-CL3996.Contig2 pair was related to anther development, miR156a-5p-CL1954.Contig2 was linked to response to high light intensity, miR408-3p/miR408d-Unigene429 was related to pollination and Unigene429 positively regulated flower development. To our knowledge, this is the first study on differential miRNA accumulation between CH and CL flowers and our study serves as a foundation to the future elucidation of regulatory mechanisms of miRNAs in the divergent development of CL and CH flowers in a single plant.

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.

Metabolic changes and associated cytokinin signals in response to nitrate assimilation in roots and shoots of Lolium perenne.

The efficiency of inorganic nitrogen (N) assimilation is a critical component of fertilizer use by plants and of forage production in Lolium perenne, an important pasture species worldwide. We present a spatiotemporal description of nitrate use efficiency in terms of metabolic responses and carbohydrate remobilization, together with components of cytokinin signal transduction following nitrate addition to N-impoverished plants. Perennial ryegrass (L. perenne cv. Grasslands Nui) plants were grown for 10 weeks in unfertilized soil and then treated with nitrate (5 mM) hydroponically. Metabolomic analysis by gas chromatography-mass spectrometry and liquid chromatography-tandem mass spectrometry revealed a dynamic interaction between N and carbon metabolism over a week-long time course represented by the relative abundance of amino acids, tricarboxylic acid intermediates and stored water-soluble carbohydrates (WSCs). The initial response to N addition was characterized by a rapid remobilization of carbon stores from the low-molecular weight WSC, along with an increase in N content and assimilation into free amino acids. Subsequently, the shoot became the main source of carbon through remobilization of a large pool of high-molecular weight WSC. Associated quantification of cytokinin levels and expression profiling of putative cytokinin response regulator genes by quantitative reverse transcription polymerase chain reaction support a role for cytokinin in the mediation of the response to N addition in perennial ryegrass. The presence of high levels of cis-zeatin-type cytokinins is discussed in the context of hormonal homeostasis under the stress of steady-state N deficiency.

A novel family of cyclic oligopeptides derived from ribosomal peptide synthesis of an in planta-induced gene, gigA, in Epichloë endophytes of grasses.

Fungal endophytes belonging to the genus Epichloë form associations with temperate grasses belonging to the sub-family Poöideae that range from mutualistic through to pathogenic. We previously identified a novel endophyte gene (designated gigA for grass induced gene) that is one of the most abundantly expressed fungal transcripts in endophyte-infected grasses and which is distributed and highly expressed in a wide range of Epichloë grass associations. Molecular and biochemical analyses indicate that gigA encodes a small secreted protein containing an imperfect 27 amino acid repeat that includes a kexin protease cleavage site. Kexin processing of GigA liberates within the plant multiple related products, named here as epichloëcyclins, which we have demonstrated by MS/MS to be cyclic peptidic in nature. Gene deletion of gigA leads to the elimination of all epichloëcyclins with no conspicuous phenotypic impact on the host grass, suggesting a possible bioactive role. This is a further example of a ribosomal peptide synthetic (RiPS) pathway operating within the Ascomycetes, and is the first description of such a pathway from a mutualistic symbiotic fungus from this Phylum.

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.

Predicting retention time in hydrophilic interaction liquid chromatography mass spectrometry and its use for peak annotation in metabolomics.

Liquid chromatography coupled to mass spectrometry (LCMS) is widely used in metabolomics due to its sensitivity, reproducibility, speed and versatility. Metabolites are detected as peaks which are characterised by mass-over-charge ratio (m/z) and retention time (rt), and one of the most critical but also the most challenging tasks in metabolomics is to annotate the large number of peaks detected in biological samples. Accurate m/z measurements enable the prediction of molecular formulae which provide clues to the chemical identity of peaks, but often a number of metabolites have identical molecular formulae. Chromatographic behaviour, reflecting the physicochemical properties of metabolites, should also provide structural information. However, the variation in rt between analytical runs, and the complicating factors underlying the observed time shifts, make the use of such information for peak annotation a non-trivial task. To this end, we conducted Quantitative Structure-Retention Relationship (QSRR) modelling between the calculated molecular descriptors (MDs) and the experimental retention times (rts) of 93 authentic compounds analysed using hydrophilic interaction liquid chromatography (HILIC) coupled to high resolution MS. A predictive QSRR model based on Random Forests algorithm outperformed a Multiple Linear Regression based model, and achieved a high correlation between predicted rts and experimental rts (Pearson's correlation coefficient = 0.97), with mean and median absolute error of 0.52 min and 0.34 min (corresponding to 5.1 and 3.2 % error), respectively. We demonstrate that rt prediction with the precision achieved enables the systematic utilisation of rts for annotating unknown peaks detected in a metabolomics study. The application of the QSRR model with the strategy we outlined enhanced the peak annotation process by reducing the number of false positives resulting from database queries by matching accurate mass alone, and enriching the reference library. The predicted rts were validated using either authentic compounds or ion fragmentation patterns.

Computational analyses of spectral trees from electrospray multi-stage mass spectrometry to aid metabolite identification.

Mass spectrometry coupled with chromatography has become the major technical platform in metabolomics. Aided by peak detection algorithms, the detected signals are characterized by mass-over-charge ratio (m/z) and retention time. Chemical identities often remain elusive for the majority of the signals. Multi-stage mass spectrometry based on electrospray ionization (ESI) allows collision-induced dissociation (CID) fragmentation of selected precursor ions. These fragment ions can assist in structural inference for metabolites of low molecular weight. Computational investigations of fragmentation spectra have increasingly received attention in metabolomics and various public databases house such data. We have developed an R package "iontree" that can capture, store and analyze MS2 and MS3 mass spectral data from high throughput metabolomics experiments. The package includes functions for ion tree construction, an algorithm (distMS2) for MS2 spectral comparison, and tools for building platform-independent ion tree (MS2/MS3) libraries. We have demonstrated the utilization of the package for the systematic analysis and annotation of fragmentation spectra collected in various metabolomics platforms, including direct infusion mass spectrometry, and liquid chromatography coupled with either low resolution or high resolution mass spectrometry. Assisted by the developed computational tools, we have demonstrated that spectral trees can provide informative evidence complementary to retention time and accurate mass to aid with annotating unknown peaks. These experimental spectral trees once subjected to a quality control process, can be used for querying public MS2 databases or de novo interpretation. The putatively annotated spectral trees can be readily incorporated into reference libraries for routine identification of metabolites.