Summary• High-temperature, low-light (HTLL) treatment of 35S:PAP1 Arabidopsis thaliana over-expressing the PAP1 (Production of Anthocyanin Pigment 1) gene results in reversible reduction of red colouration, suggesting the action of additional anthocyanin regulators. High-performance liquid chromatography (HPLC), liquid chromatography mass spectrometry (LCMS) and Affimetrix ® -based microarrays were used to measure changes in anthocyanin, flavonoids, and gene expression in response to HTLL.• HTLL treatment of control and 35S:PAP1 A. thaliana resulted in a reversible reduction in the concentrations of major anthocyanins despite ongoing over-expression of the PAP1 MYB transcription factor. Twenty-one anthocyanins including eight ciscoumaryl esters were identified by LCMS. The concentrations of nine anthocyanins were reduced and those of three were increased, consistent with a sequential process of anthocyanin degradation. Analysis of gene expression showed down-regulation of flavonol and anthocyanin biosynthesis and of transport-related genes within 24 h of HTLL treatment. No catabolic genes up-regulated by HTLL were found.• Reductions in the concentrations of anthocyanins and down-regulation of the genes of anthocyanin biosynthesis were achieved by environmental manipulation, despite ongoing over-expression of PAP1. Quantitative PCR showed reduced expression of three genes (TT8, TTG1 and EGL3) of the PAP1 transcriptional complex, and increased expression of the potential transcriptional repressors AtMYB3, AtMYB6 and AtMYBL2 coincided with HTLL-induced down-regulation of anthocyanin biosynthesis.• HTLL treatment offers a model system with which to explore anthocyanin catabolism and to discover novel genes involved in the environmental control of anthocyanins.

Key messageGenomic 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.AbstractPerennial 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.Electronic supplementary materialThe online version of this article (10.1007/s00122-017-3030-1) contains supplementary material, which is available to authorized users.

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Direct-infusion mass spectrometry (MS) was applied to study the metabolic effects of the symbiosis between the endophytic fungus Neotyphodium lolii and its host perennial ryegrass (Lolium perenne) in three different tissues (immature leaf, blade, and sheath). Unbiased direct-infusion MS using a linear ion trap mass spectrometer allowed metabolic effects to be determined free of any preconceptions and in a high-throughput fashion. Not only the full MS 1 mass spectra (range 150-1,000 mass-to-charge ratio) were obtained but also MS 2 and MS 3 product ion spectra were collected on the most intense MS 1 ions as described previously (Koulman et al., 2007b). We developed a novel computational methodology to take advantage of the MS 2 product ion spectra collected. Several heterogeneous MS 1 bins (different MS 2 spectra from the same nominal MS 1 ) were identified with this method. Exploratory data analysis approaches were also developed to investigate how the metabolome differs in perennial ryegrass infected with N. lolii in comparison to uninfected perennial ryegrass. As well as some known fungal metabolites like peramine and mannitol, several novel metabolites involved in the symbiosis, including putative cyclic oligopeptides, were identified. Correlation network analysis revealed a group of structurally related oligosaccharides, which differed significantly in concentration in perennial ryegrass sheaths due to endophyte infection. This study demonstrates the potential of the combination of unbiased metabolite profiling using ion trap MS and advanced data-mining strategies for discovering unexpected perturbations of the metabolome, and generating new scientific questions for more detailed investigations in the future.With the advent of metabolomics, methods for the simultaneous analysis of a large number of small molecules (metabolites) have been developed and improved, providing more details about the metabolism of complex biological systems (Sumner et al., 2003;Dettmer et al., 2007). Metabolite fingerprinting methods provide relatively unbiased and high-throughput information on complex biological systems and have been used for yeast (Saccharomyces cerevisiae) strain classification (Allen et al., 2003) and annotation of gene functions (Raamsdonk et al., 2001). Comprehensive and unbiased metabolite analysis is also an indispensable tool for systems biology together with transcriptomics and proteomics (see commentary by Sauer et al., 2007). Direct-infusion (without prior chromatographic separation) electrospray ionization (ESI) mass spectrometry (MS) was introduced as a tool for the identification of novel fungal metabolites in culture (Smedsgaard and Frisvad, 1996) and is now widely applied in metabolomics (for a recent review, see Dettmer et al., 2007). High resolution MS instrumentation such as time-of-flight MS (Dunn et al., 2005) or Fourier transform ion cyclotron resonance MS (FT-ICR-MS; Aharoni et al., 2002) is often preferred for the specificity provided by resolution of isobaric ions and highly accurate e...

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.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-014-0727-x) contains supplementary material, which is available to authorized users.