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Bornhofen E, Fè D, Nagy I, Lenk I, Greve M, Didion T, Jensen CS, Asp T, Janss L. Genetic architecture of inter-specific and -generic grass hybrids by network analysis on multi-omics data. BMC Genomics 2023; 24:213. [PMID: 37095447 PMCID: PMC10127077 DOI: 10.1186/s12864-023-09292-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 04/02/2023] [Indexed: 04/26/2023] Open
Abstract
BACKGROUND Understanding the mechanisms underlining forage production and its biomass nutritive quality at the omics level is crucial for boosting the output of high-quality dry matter per unit of land. Despite the advent of multiple omics integration for the study of biological systems in major crops, investigations on forage species are still scarce. RESULTS Our results identified substantial changes in gene co-expression and metabolite-metabolite network topologies as a result of genetic perturbation by hybridizing L. perenne with another species within the genus (L. multiflorum) relative to across genera (F. pratensis). However, conserved hub genes and hub metabolomic features were detected between pedigree classes, some of which were highly heritable and displayed one or more significant edges with agronomic traits in a weighted omics-phenotype network. In spite of tagging relevant biological molecules as, for example, the light-induced rice 1 (LIR1), hub features were not necessarily better explanatory variables for omics-assisted prediction than features stochastically sampled and all available regressors. CONCLUSIONS The utilization of computational techniques for the reconstruction of co-expression networks facilitates the identification of key omic features that serve as central nodes and demonstrate correlation with the manifestation of observed traits. Our results also indicate a robust association between early multi-omic traits measured in a greenhouse setting and phenotypic traits evaluated under field conditions.
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Affiliation(s)
- Elesandro Bornhofen
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark.
| | - Dario Fè
- Research Division, DLF Seeds A/S, Store Heddinge, Denmark
| | - Istvan Nagy
- Center for Quantitative Genetics and Genomics, Aarhus University, Slagelse, Denmark
| | - Ingo Lenk
- Research Division, DLF Seeds A/S, Store Heddinge, Denmark
| | - Morten Greve
- Research Division, DLF Seeds A/S, Store Heddinge, Denmark
| | - Thomas Didion
- Research Division, DLF Seeds A/S, Store Heddinge, Denmark
| | | | - Torben Asp
- Center for Quantitative Genetics and Genomics, Aarhus University, Slagelse, Denmark
| | - Luc Janss
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark.
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Meng Y, Du Q, Du H, Wang Q, Wang L, Du L, Liu P. Analysis of chemotypes and their markers in leaves of core collections of Eucommia ulmoides using metabolomics. FRONTIERS IN PLANT SCIENCE 2023; 13:1029907. [PMID: 36699853 PMCID: PMC9868706 DOI: 10.3389/fpls.2022.1029907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 12/02/2022] [Indexed: 06/17/2023]
Abstract
The leaves of Eucommia ulmoides contain various active compunds and nutritional components, and have successively been included as raw materials in the Chinese Pharmacopoeia, the Health Food Raw Material Catalogue, and the Feed Raw Material Catalogue. Core collections of E. ulmoides had been constructed from the conserved germplasm resources basing on molecular markers and morphological traits, however, the metabolite diversity and variation in this core population were little understood. Metabolite profiles of E. ulmoides leaves of 193 core collections were comprehensively characterized by GC-MS and LC-MS/MS based non-targeted metabolomics in present study. Totally 1,100 metabolites were identified and that belonged to 18 categories, and contained 120 active ingredients for traditional Chinese medicine (TCM) and 85 disease-resistant metabolites. Four leaf chemotypes of the core collections were established by integrated uses of unsupervised self-organizing map (SOM), supervised orthogonal partial least squares discriminant analysis (OPLS-DA) and random forest (RF) statistical methods, 30, 23, 43, and 23 chemomarkers were screened corresponding to the four chemotypes, respectively. The morphological markers for the chemotypes were obtained by weighted gene co-expression network analysis (WGCNA) between the chenomarkers and the morphological traits, with leaf length (LL), chlorophyll reference value (CRV), leaf dentate height (LDH), and leaf thickness (LT) corresponding to chemotypes I, II, III, and IV, respectively. Contents of quercetin-3-O-pentosidine, isoquercitrin were closely correlated to LL, leaf area (LA), and leaf perimeter (LP), suggesting the quercetin derivatives might influence the growth and development of E. ulmoides leaf shape.
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Affiliation(s)
- Yide Meng
- Research Institute of Non-Timber Forestry, Chinese Academy of Forestry, Zhengzhou, China
| | - Qingxin Du
- Research Institute of Non-Timber Forestry, Chinese Academy of Forestry, Zhengzhou, China
| | - Hongyan Du
- Research Institute of Non-Timber Forestry, Chinese Academy of Forestry, Zhengzhou, China
| | - Qi Wang
- Research Institute of Non-Timber Forestry, Chinese Academy of Forestry, Zhengzhou, China
| | - Lu Wang
- Research Institute of Non-Timber Forestry, Chinese Academy of Forestry, Zhengzhou, China
| | - Lanying Du
- Research Institute of Non-Timber Forestry, Chinese Academy of Forestry, Zhengzhou, China
| | - Panfeng Liu
- Research Institute of Non-Timber Forestry, Chinese Academy of Forestry, Zhengzhou, China
- Key Laboratory of Non-timber Forest Germplasm Enhancement & Utilization of National Forestry and Grassland Administration, Zhengzhou, China
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Li J, Huang T, Lu J, Xu X, Zhang W. Metabonomic profiling of clubroot-susceptible and clubroot-resistant radish and the assessment of disease-resistant metabolites. FRONTIERS IN PLANT SCIENCE 2022; 13:1037633. [PMID: 36570889 PMCID: PMC9772615 DOI: 10.3389/fpls.2022.1037633] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 11/16/2022] [Indexed: 06/17/2023]
Abstract
Plasmodiophora brassicae causes a serious threat to cruciferous plants including radish (Raphanus sativus L.). Knowledge on the pathogenic regularity and molecular mechanism of P. brassicae and radish is limited, especially on the metabolism level. In the present study, clubroot-susceptible and clubroot-resistant cultivars were inoculated with P. brassicae Race 4, root hairs initial infection of resting spores (107 CFU/mL) at 24 h post-inoculation and root galls symptom arising at cortex splitting stage were identified on both cultivars. Root samples of cortex splitting stage of two cultivars were collected and used for untargeted metabonomic analysis. We demonstrated changes in metabolite regulation and pathways during the cortex splitting stage of diseased roots between clubroot-susceptible and clubroot-resistant cultivars using untargeted metabonomic analysis. We identified a larger number of differentially regulated metabolites and heavier metabolite profile changes in the susceptible cultivar than in the resistant counterpart. The metabolites that were differentially regulated in both cultivars were mostly lipids and lipid-like molecules. Significantly regulated metabolites and pathways according to the P value and variable important in projection score were identified. Moreover, four compounds, including ethyl α-D-thioglucopyranoside, imipenem, ginsenoside Rg1, and 6-gingerol, were selected, and their anti-P. brassicae ability and effects on seedling growth were verified on the susceptible cultivar. Except for ethyl α-D-thioglucopyranoside, the remaining could inhibit clubroot development of varing degree. The use of 5 mg/L ginsenoside Rg1 + 5 mg/L 6-gingerol resulted in the lowest disease incidence and disease index among all treatments and enhanced seedling growth. The regulation of pathways or metabolites of carbapenem and ginsenoside was further explored. The results provide a preliminary understanding of the interaction between radish and P. brassicae at the metabolism level, as well as the development of measures for preventing clubroot.
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Affiliation(s)
- Jingwei Li
- Vegetable Research Institute, Guizhou University, Guiyang, China
- College of Agriculture, Guizhou University, Guiyang, China
| | - Tingmin Huang
- Vegetable Research Institute, Guizhou University, Guiyang, China
- College of Agriculture, Guizhou University, Guiyang, China
| | - Jinbiao Lu
- Vegetable Research Institute, Guizhou University, Guiyang, China
- College of Agriculture, Guizhou University, Guiyang, China
| | - Xiuhong Xu
- Vegetable Research Institute, Guizhou University, Guiyang, China
- College of Agriculture, Guizhou University, Guiyang, China
| | - Wanping Zhang
- Vegetable Research Institute, Guizhou University, Guiyang, China
- College of Agriculture, Guizhou University, Guiyang, China
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Untargeted Multimodal Metabolomics Investigation of the Haemonchus contortus Exsheathment Secretome. Cells 2022; 11:cells11162525. [PMID: 36010603 PMCID: PMC9406637 DOI: 10.3390/cells11162525] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/11/2022] [Accepted: 08/13/2022] [Indexed: 11/17/2022] Open
Abstract
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.
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Koley S, Chu KL, Gill SS, Allen DK. An efficient LC-MS method for isomer separation and detection of sugars, phosphorylated sugars, and organic acids. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:2938-2952. [PMID: 35560196 DOI: 10.1093/jxb/erac062] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Accepted: 02/15/2022] [Indexed: 06/15/2023]
Abstract
Assessing central carbon metabolism in plants can be challenging due to the dynamic range in pool sizes, with low levels of important phosphorylated sugars relative to more abundant sugars and organic acids. Here, we report a sensitive liquid chromatography-mass spectrometry method for analysing central metabolites on a hybrid column, where both anion-exchange and hydrophilic interaction chromatography (HILIC) ligands are embedded in the stationary phase. The liquid chromatography method was developed for enhanced selectivity of 27 central metabolites in a single run with sensitivity at femtomole levels observed for most phosphorylated sugars. The method resolved phosphorylated hexose, pentose, and triose isomers that are otherwise challenging. Compared with a standard HILIC approach, these metabolites had improved peak areas using our approach due to ion enhancement or low ion suppression in the biological sample matrix. The approach was applied to investigate metabolism in high lipid-producing tobacco leaves that exhibited increased levels of acetyl-CoA, a precursor for oil biosynthesis. The application of the method to isotopologue detection and quantification was considered through evaluating 13C-labeled seeds from Camelina sativa. The method provides a means to analyse intermediates more comprehensively in central metabolism of plant tissues.
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Affiliation(s)
- Somnath Koley
- Donald Danforth Plant Science Center, St Louis, MO 63132, USA
| | - Kevin L Chu
- Donald Danforth Plant Science Center, St Louis, MO 63132, USA
- United States Department of Agriculture-Agriculture Research Service, Donald Danforth Plant Science Center, St Louis, MO 63132, USA
| | - Saba S Gill
- Donald Danforth Plant Science Center, St Louis, MO 63132, USA
- United States Department of Agriculture-Agriculture Research Service, Donald Danforth Plant Science Center, St Louis, MO 63132, USA
| | - Doug K Allen
- Donald Danforth Plant Science Center, St Louis, MO 63132, USA
- United States Department of Agriculture-Agriculture Research Service, Donald Danforth Plant Science Center, St Louis, MO 63132, USA
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Fukushima A, Takahashi M, Nagasaki H, Aono Y, Kobayashi M, Kusano M, Saito K, Kobayashi N, Arita M. Development of RIKEN Plant Metabolome MetaDatabase. PLANT & CELL PHYSIOLOGY 2022; 63:433-440. [PMID: 34918130 PMCID: PMC8917833 DOI: 10.1093/pcp/pcab173] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 11/15/2021] [Accepted: 12/16/2021] [Indexed: 06/14/2023]
Abstract
The advancement of metabolomics in terms of techniques for measuring small molecules has enabled the rapid detection and quantification of numerous cellular metabolites. Metabolomic data provide new opportunities to gain a deeper understanding of plant metabolism that can improve the health of both plants and humans that consume them. Although major public repositories for general metabolomic data have been established, the community still has shortcomings related to data sharing, especially in terms of data reanalysis, reusability and reproducibility. To address these issues, we developed the RIKEN Plant Metabolome MetaDatabase (RIKEN PMM, http://metabobank.riken.jp/pmm/db/plantMetabolomics), which stores mass spectrometry-based (e.g. gas chromatography-MS-based) metabolite profiling data of plants together with their detailed, structured experimental metadata, including sampling and experimental procedures. Our metadata are described as Linked Open Data based on the Resource Description Framework using standardized and controlled vocabularies, such as the Metabolomics Standards Initiative Ontology, which are to be integrated with various life and biomedical science data using the World Wide Web. RIKEN PMM implements intuitive and interactive operations for plant metabolome data, including raw data (netCDF format), mass spectra (NIST MSP format) and metabolite annotations. The feature is suitable not only for biologists who are interested in metabolomic phenotypes, but also for researchers who would like to investigate life science in general through plant metabolomic approaches.
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Affiliation(s)
- Atsushi Fukushima
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
| | - Mikiko Takahashi
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
| | - Hideki Nagasaki
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
| | - Yusuke Aono
- Degree Programs in Life and Earth Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan
| | - Makoto Kobayashi
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
| | - Miyako Kusano
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
- Faculty of Life and Environmental Science, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan
- Tsukuba Plant Innovation Research Center, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan
| | - Kazuki Saito
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
| | - Norio Kobayashi
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
- Data Knowledge Organization Unit, RIKEN Information R&D and Strategy Headquarters, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Masanori Arita
- Metabolome Informatics Research Team, RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro, Tsurumi, Yokohama, Kanagawa 230-0045, Japan
- Bioinformation and DDBJ Center, National Institute of Genetics, Yata 1111, Mishima, Shizuoka 411-8540, Japan
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DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies. Sci Rep 2021; 11:5657. [PMID: 33707505 PMCID: PMC7952378 DOI: 10.1038/s41598-021-84824-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 02/19/2021] [Indexed: 02/07/2023] Open
Abstract
As a powerful phenotyping technology, metabolomics provides new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and the identification of metabolites having a regulatory effect in various biological processes. While mass spectrometry-based (MS) metabolomics assays are endowed with high throughput and sensitivity, MWAS are doomed to long-term data acquisition generating an overtime-analytical signal drift that can hinder the uncovering of real biologically relevant changes. We developed “dbnorm”, a package in the R environment, which allows for an easy comparison of the model performance of advanced statistical tools commonly used in metabolomics to remove batch effects from large metabolomics datasets. “dbnorm” integrates advanced statistical tools to inspect the dataset structure not only at the macroscopic (sample batches) scale, but also at the microscopic (metabolic features) level. To compare the model performance on data correction, “dbnorm” assigns a score that help users identify the best fitting model for each dataset. In this study, we applied “dbnorm” to two large-scale metabolomics datasets as a proof of concept. We demonstrate that “dbnorm” allows for the accurate selection of the most appropriate statistical tool to efficiently remove the overtime signal drift and to focus on the relevant biological components of complex datasets.
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Distel RA, Arroquy JI, Lagrange S, Villalba JJ. Designing Diverse Agricultural Pastures for Improving Ruminant Production Systems. FRONTIERS IN SUSTAINABLE FOOD SYSTEMS 2020. [DOI: 10.3389/fsufs.2020.596869] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
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Shorten PR, Leath SR, Schmidt J, Ghamkhar K. Predicting the quality of ryegrass using hyperspectral imaging. PLANT METHODS 2019; 15:63. [PMID: 31182971 PMCID: PMC6554905 DOI: 10.1186/s13007-019-0448-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Accepted: 05/28/2019] [Indexed: 05/08/2023]
Abstract
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 (R2 validation of 0.58), high molecular weight sugars (R2 validation of 0.63), %Ash (R2 validation of 0.50) and %Nitrogen (R2 validation of 0.70). Significant HSI models with low R2 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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Affiliation(s)
- Paul R. Shorten
- AgResearch, Ruakura Research Centre, Private Bag 3123, Hamilton, 3240 New Zealand
| | - Shane R. Leath
- AgResearch, Ruakura Research Centre, Private Bag 3123, Hamilton, 3240 New Zealand
| | - Jana Schmidt
- AgResearch, Grasslands Research Centre, Private Bag 11008, Palmerston North, 4442 New Zealand
| | - Kioumars Ghamkhar
- AgResearch, Grasslands Research Centre, Private Bag 11008, Palmerston North, 4442 New Zealand
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