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

Subbaraj, Arvind K.; Huege, Jan; Fraser, Karl; Cao, Mingshu; Rasmussen, Susanne; Faville, Marty J.; Harrison, S. P.; Jones, Chris S.

doi:10.1038/s42003-019-0289-6

Cited by 16 publications

(19 citation statements)

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“…WSC were determined by liquid/gas chromatography–mass spectrometry based technique [35]. Dry matter and Ash/OM were determined by AOAC 930.15/925.10/942.05.…”

Section: Methodsmentioning

confidence: 99%

Predicting the quality of ryegrass using hyperspectral imaging

et al. 2019

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Background The quality of forage plants is a crucial component of animal performance and a limiting factor in pasture based production systems. Key forage attributes that may require improvement include the sugar, lipid, protein and energy contents of the vegetative parts of these plants. The aim of this study was to evaluate the potential capacity of hyperspectral imaging (HSI) for non-invasive assessment of forage chemical composition. Hyperspectral image data within the visible near-infrared range into the extended near-infrared covering 550–1700 nm wavelengths were obtained from 185 accessions of ryegrass ( Lolium perenne ), which were also analysed for 13 forage quality attributes. Results Medium to high predictive power was observed for the HSI models of total sugars (R 2 validation of 0.58), high molecular weight sugars (R 2 validation of 0.63), %Ash (R 2 validation of 0.50) and %Nitrogen (R 2 validation of 0.70). Significant HSI models with low R 2 validation of 0.1–0.5 were also obtained for low molecular weight sugars, NDF (%), ADF (%), DOMD (% DM), ME (MJ/kg DM), DM (%), Ca (mg/g) and OM (%). We also observed significant differences in the chemical composition between the pseudostems and leaves of the plants for each accession. The power of HSI for prediction of these differences within plants was also demonstrated. Conclusion This study paves the way for the HSI technology to be used for in-field estimation of forage composition attributes in perennial ryegrass. This will allow more rapid genetic-based selection and breeding for a trait that is normally expensive to measure providing a cheaper, non-destructive and high throughput screening tool.

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“…WSC were determined by liquid/gas chromatography–mass spectrometry based technique [35]. Dry matter and Ash/OM were determined by AOAC 930.15/925.10/942.05.…”

Section: Methodsmentioning

confidence: 99%

Predicting the quality of ryegrass using hyperspectral imaging

et al. 2019

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“…An overview of the extraction protocol and treatment of samples in preparation for metabolomics and lipidomics analyses is shown in Supplementary Figure S1. Briefly, samples were thawed overnight at 4℃, centrifuged (4℃, 11,000 × g) for For polar and semi-polar compounds, hydrophilic interaction liquid chromatography (HILIC) and C18 chromatography were applied, respectively (41)(42)(43). For these analyses, all procedures were similar to that of lipids, except that the extraction solvents comprised of acetonitrile:water containing 0.1% formic acid (1:1 for HILIC and 1:9 for C18, v/v).…”

Section: In Vitro Larval Exsheathment Assaymentioning

confidence: 99%

“…The mobile phase was a mixture of water with 0.1% formic acid (solvent A), and acetonitrile with 0.1% formic acid (solvent B). Chromatographic gradient and other LC-MS conditions have been previously described (43,46). For polar compounds, HILIC conditions were set as described (41) by extract (2 µL) was injected into a 100 mm × 2.1 mm ZIC-pHILIC column…”

Section: Chromatography and Mass Spectrometry Conditionsmentioning

confidence: 99%

Non-targeted multimodal metabolomics data from ovine rumen fluid fractions

Palevich

Maclean

Subbaraj

et al. 2021

Preprint

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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 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 nematode, Haemonchus contortus (barber‘s pole worm). This project applied the basic principles of dialysis and is the first reported use of all currently available non-specific metabolomics methodologies to identify the rumen-derived trigger compounds associated with larval development in H. contortus. Herein, we report an multimodal untargeted metabolomics and lipidomics analyses based on 20 kDa (n = 10), 8-10 kDa (n = 10) and 100 Da (n = 10) molecular weight cut-off dialyzed rumen fluid fractions, all of which demonstrated positive effects on exsheathment induction in vitro under rumen conditions, as compared to control infective third-stage larva (L3) in saline (n = 10). Metabolite identification and multivariate analyses resulted in a catalogue of over one thousand structurally annotated compounds or metabolites that form three distinct cluster groups and warrant further investigation. The multimodal metabolomics analyses revealed significant alterations between the three different rumen fluid fractions in L-lactic acid, pyruvate metabolism, gluconeogenesis, the Warburg effect, plant-pathogen interaction, plant hormone signal transduction, and biosynthesis of amino acids metabolism. Lysophosphatidylglycerol, diglyceride, phosphatidylcholine, triglyceride and a sterol were the major lipid classes identified by the non-targeted lipidomics method applied. A combination of univariate and multivariate statistical analyses revealed a panel of metabolites directly responsible for larval exsheathment and associated with amino acids as well as purine and pyruvate metabolism KEGG pathways. Among these were compounds such as methionine, leucine, alanine, phenylalanine, xanthine and hypoxanthine, that were significant in the control group and identified using two separate metabolomics streams. This study presents an in vitro system suitable for numerous functional omics’ experimentation applications and has enhanced our understanding of the distinct molecular and metabolic pathways associated with larval exsheathment. The identified metabolites may serve as metabolic signatures or novel metabolic biomarkers for pre-diagnostic purposes of haemonchosis. Alternatively, our findings can be used to develop next-generation biomimicry agents to cause infective larvae of parasitic nematodes to exsheath outside the host while on pasture for experimental or control purposes.

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“…These statistical models offer the flexibility to accommodate signal variation across multiple batches based only on the sequence of acquisition, or integrating also other available population demographic characteristics (i.e. treatment) 9,14 , which is recommended in case of uneven study design.…”

Section: Statistical Modeling For Intra-and Inter-batch Signal Drift mentioning

confidence: 99%

“…On the other hand, LIMMA model strictly dependent on some additional information on sample metadata such as biological covariates, which are used for the design of the model matrix 41 . In contrast, ComBat has the flexibility to adjust batch effect by integrating information on both batches and biological covariates or just on batches if demography of population is not available 9,14 . This function is developed in the global term of "Parametric shrinkage adjustment" and accommodates batch effect in three steps: data standardization, batch effect estimation via empirical priors and batch effect removal using the adjusted estimators 45 .…”

Section: Introductionmentioning

confidence: 99%

Visualization and normalization of drift effect across batches in metabolome-wide association studies

Bararpour

Gilardi

Carmeli

et al. 2020

Preprint

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As a powerful phenotyping technology, metabolomics has provided new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and identification of metabolites having regulatory effect in various biological processes.While MAS-based metabolomics assays are endowed with high-throughput and sensitivity, large-scale MWAS are doomed to long-term data acquisition generating an overtime-analytical signal drift, inherent to liquid chromatography-MS technique that can hinder the uncovering of true biologically relevant changes.We developed "dbnorm", a package in R environment, which allows visualization and removal of signal heterogeneity from large metabolomics datasets. "dbnorm" integrates advanced statistical tools to inspect dataset structure, at both macroscopic (sample batch) and microscopic (metabolic features) scales. To compare model performance on data correction, "dbnorm" assigns a score, which allows the straightforward identification of the best fitting model for each dataset. Herein, we show how "dbnorm" efficiently removes signal drift among batches to capture the true biological heterogeneity of data in two large-scale metabolomics studies.

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

Cited by 16 publications

References 73 publications

Predicting the quality of ryegrass using hyperspectral imaging

Predicting the quality of ryegrass using hyperspectral imaging

Non-targeted multimodal metabolomics data from ovine rumen fluid fractions

Visualization and normalization of drift effect across batches in metabolome-wide association studies

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