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      A Multi-Omics Analysis Pipeline for the Metabolic Pathway Reconstruction in the Orphan Species Quercus ilex

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          Abstract

          Holm oak ( Quercus ilex) is the most important and representative species of the Mediterranean forest and of the Spanish agrosilvo-pastoral “dehesa” ecosystem. Despite its environmental and economic interest, Holm oak is an orphan species whose biology is very little known, especially at the molecular level. In order to increase the knowledge on the chemical composition and metabolism of this tree species, the employment of a holistic and multi-omics approach, in the Systems Biology direction would be necessary. However, for orphan and recalcitrant plant species, specific analytical and bioinformatics tools have to be developed in order to obtain adequate quality and data-density before to coping with the study of its biology. By using a plant sample consisting of a pool generated by mixing equal amounts of homogenized tissue from acorn embryo, leaves, and roots, protocols for transcriptome (NGS-Illumina), proteome (shotgun LC-MS/MS), and metabolome (GC-MS) studies have been optimized. These analyses resulted in the identification of around 62629 transcripts, 2380 protein species, and 62 metabolites. Data are compared with those reported for model plant species, whose genome has been sequenced and is well annotated, including Arabidopsis, japonica rice, poplar, and eucalyptus. RNA and protein sequencing favored each other, increasing the number and confidence of the proteins identified and correcting erroneous RNA sequences. The integration of the large amount of data reported using bioinformatics tools allows the Holm oak metabolic network to be partially reconstructed: from the 127 metabolic pathways reported in KEGG pathway database, 123 metabolic pathways can be visualized when using the described methodology. They included: carbohydrate and energy metabolism, amino acid metabolism, lipid metabolism, nucleotide metabolism, and biosynthesis of secondary metabolites. The TCA cycle was the pathway most represented with 5 out of 10 metabolites, 6 out of 8 protein enzymes, and 8 out of 8 enzyme transcripts. On the other hand, gaps, missed pathways, included metabolism of terpenoids and polyketides and lipid metabolism. The multi-omics resource generated in this work will set the basis for ongoing and future studies, bringing the Holm oak closer to model species, to obtain a better understanding of the molecular mechanisms underlying phenotypes of interest (productive, tolerant to environmental cues, nutraceutical value) and to select elite genotypes to be used in restoration and reforestation programs, especially in a future climate change scenario.

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          Untargeted Metabolomics Strategies—Challenges and Emerging Directions

          Metabolites are building blocks of cellular function. These species are involved in enzyme-catalyzed chemical reactions and are essential for cellular function. Upstream biological disruptions result in a series of metabolomic changes, and as such the metabolome holds a wealth of information that is thought to be most predictive of phenotype. Uncovering this knowledge is a work in progress. The field of metabolomics is still maturing; the community has leveraged proteomics experience when applicable and developed a range of sample preparation and instrument methodology along with myriad data processing and analysis approaches. Research focuses have now shifted toward a fundamental understanding of the biology responsible for metabolomic changes. There are several types of metabolomics experiments including both targeted and untargeted analyses. While untargeted, hypothesis generating, workflows exhibit many valuable attributes, challenges inherent to the approach remain. This Critical Insight comments on these challenges, focusing on the identification process of LC-MS based untargeted metabolomics studies – specifically in mammalian systems. Biological interpretation of metabolomics data hinges on the ability to accurately identify metabolites. The range of confidence associated with identifications that is often overlooked is reviewed, and opportunities for advancing the metabolomics field are described.
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            Mercator: a fast and simple web server for genome scale functional annotation of plant sequence data.

            Next-generation technologies generate an overwhelming amount of gene sequence data. Efficient annotation tools are required to make these data amenable to functional genomics analyses. The Mercator pipeline automatically assigns functional terms to protein or nucleotide sequences. It uses the MapMan 'BIN' ontology, which is tailored for functional annotation of plant 'omics' data. The classification procedure performs parallel sequence searches against reference databases, compiles the results and computes the most likely MapMan BINs for each query. In the current version, the pipeline relies on manually curated reference classifications originating from the three reference organisms (Arabidopsis, Chlamydomonas, rice), various other plant species that have a reviewed SwissProt annotation, and more than 2000 protein domain and family profiles at InterPro, CDD and KOG. Functional annotations predicted by Mercator achieve accuracies above 90% when benchmarked against manual annotation. In addition to mapping files for direct use in the visualization software MapMan, Mercator provides graphical overview charts, detailed annotation information in a convenient web browser interface and a MapMan-to-GO translation table to export results as GO terms. Mercator is available free of charge via http://mapman.gabipd.org/web/guest/app/Mercator. © 2013 John Wiley & Sons Ltd.
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              A guide to using MapMan to visualize and compare Omics data in plants: a case study in the crop species, Maize.

              MapMan is a software tool that supports the visualization of profiling data sets in the context of existing knowledge. Scavenger modules generate hierarchical and essentially non-redundant gene ontologies ('mapping files'). An ImageAnnotator module visualizes the data on a gene-by-gene basis on schematic diagrams ('maps') of biological processes. The PageMan module uses the same ontologies to statistically evaluate responses at the pathway or processes level. The generic structure of MapMan also allows it to be used for transcripts, proteins, enzymes and metabolites. MapMan was developed for use with Arabidopsis, but has already been extended for use with several other species. These tools are available as downloadable and web-based versions. After providing an introduction to the scope and use of MapMan, we present a case study where MapMan is used to analyse the transcriptional response of the crop plant maize to diurnal changes and an extension of the night. We then explain how MapMan can be customized to visually and systematically compare responses in maize and Arabidopsis. These analyses illustrate how MapMan can be used to analyse and compare global transcriptional responses between phylogenetically distant species, and show that analyses at the level of functional categories are especially useful in cross-species comparisons.
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                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                11 July 2018
                2018
                : 9
                : 935
                Affiliations
                [1] 1Agroforestry and Plant Biochemistry and Proteomics Research Group, Department Biochemistry and Molecular Biology, Universidad de Córdoba , Córdoba, Spain
                [2] 2Instituto de Agricultura Sostenible , Córdoba, Spain
                [3] 3Departamento de Biología de Organismos y Sistemas, Universidad de Oviedo , Oviedo, Spain
                Author notes

                Edited by: Atsushi Fukushima, RIKEN, Japan

                Reviewed by: Raquel Esteban, University of the Basque Country (UPV/EHU), Spain; Autar Krishen Mattoo, United States Department of Agriculture, United States

                *Correspondence: Cristina López-Hidalgo, n12lohic@ 123456uco.es Jesus V. Jorrín-Novo, bf1jonoj@ 123456uco.es

                These authors have contributed equally to this work.

                This article was submitted to Plant Systems and Synthetic Biology, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2018.00935
                6050436
                30050544
                a9fad5cf-dd16-4408-828a-79ffeaf95992
                Copyright © 2018 López-Hidalgo, Guerrero-Sánchez, Gómez-Gálvez, Sánchez-Lucas, Castillejo-Sánchez, Maldonado-Alconada, Valledor and Jorrín-Novo.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 02 November 2017
                : 11 June 2018
                Page count
                Figures: 5, Tables: 2, Equations: 0, References: 65, Pages: 16, Words: 0
                Funding
                Funded by: Ministerio de Economía y Competitividad 10.13039/501100003329
                Award ID: BIO2015-64737-R
                Categories
                Plant Science
                Original Research

                Plant science & Botany
                quercus ilex,omics,metabolome,proteome,transcriptome
                Plant science & Botany
                quercus ilex, omics, metabolome, proteome, transcriptome

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