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Li S, Hui L, Li J, Xi Y, Xu J, Wang L, Yin L. OsMGD1-Mediated Membrane Lipid Remodeling Improves Salt Tolerance in Rice. PLANTS (BASEL, SWITZERLAND) 2024; 13:1474. [PMID: 38891283 PMCID: PMC11174947 DOI: 10.3390/plants13111474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 05/23/2024] [Accepted: 05/23/2024] [Indexed: 06/21/2024]
Abstract
Salt stress severely reduces photosynthetic efficiency, resulting in adverse effects on crop growth and yield production. Two key thylakoid membrane lipid components, monogalactosyldiacylglycerol (MGDG) and digalactosyldiacylglycerol (DGDG), were perturbed under salt stress. MGDG synthase 1 (MGD1) is one of the key enzymes for the synthesis of these galactolipids. To investigate the function of OsMGD1 in response to salt stress, the OsMGD1 overexpression (OE) and RNA interference (Ri) rice lines, and a wild type (WT), were used. Compared with WT, the OE lines showed higher chlorophyll content and biomass under salt stress. Besides this, the OE plants showed improved photosynthetic performance, including light absorption, energy transfer, and carbon fixation. Notably, the net photosynthetic rate and effective quantum yield of photosystem II in the OE lines increased by 27.5% and 25.8%, respectively, compared to the WT. Further analysis showed that the overexpression of OsMGD1 alleviated the negative effects of salt stress on photosynthetic membranes and oxidative defense by adjusting membrane lipid composition and fatty acid levels. In summary, OsMGD1-mediated membrane lipid remodeling enhanced salt tolerance in rice by maintaining membrane stability and optimizing photosynthetic efficiency.
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Affiliation(s)
- Shasha Li
- State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, College of Natural Resources and Environment, Northwest A&F University, Yangling, Xianyang 712100, China; (S.L.); (L.H.); (Y.X.); (J.X.)
- Institute of Soil and Water Conservation, College of Soil and Water Conservation Science and Engineering, Northwest A&F University, Yangling, Xianyang 712100, China
| | - Lei Hui
- State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, College of Natural Resources and Environment, Northwest A&F University, Yangling, Xianyang 712100, China; (S.L.); (L.H.); (Y.X.); (J.X.)
- Institute of Soil and Water Conservation, College of Soil and Water Conservation Science and Engineering, Northwest A&F University, Yangling, Xianyang 712100, China
| | - Jingchong Li
- Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, Xianyang 712100, China;
| | - Yuan Xi
- State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, College of Natural Resources and Environment, Northwest A&F University, Yangling, Xianyang 712100, China; (S.L.); (L.H.); (Y.X.); (J.X.)
- Institute of Soil and Water Conservation, College of Soil and Water Conservation Science and Engineering, Northwest A&F University, Yangling, Xianyang 712100, China
| | - Jili Xu
- State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, College of Natural Resources and Environment, Northwest A&F University, Yangling, Xianyang 712100, China; (S.L.); (L.H.); (Y.X.); (J.X.)
- Institute of Soil and Water Conservation, College of Soil and Water Conservation Science and Engineering, Northwest A&F University, Yangling, Xianyang 712100, China
| | - Linglong Wang
- College of Agronomy, Northwest A&F University, Yangling, Xianyang 712100, China;
| | - Lina Yin
- State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, College of Natural Resources and Environment, Northwest A&F University, Yangling, Xianyang 712100, China; (S.L.); (L.H.); (Y.X.); (J.X.)
- Institute of Soil and Water Conservation, College of Soil and Water Conservation Science and Engineering, Northwest A&F University, Yangling, Xianyang 712100, China
- Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, Xianyang 712100, China;
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2
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Yaschenko AE, Alonso JM, Stepanova AN. Arabidopsis as a model for translational research. THE PLANT CELL 2024:koae065. [PMID: 38411602 DOI: 10.1093/plcell/koae065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/26/2024] [Accepted: 01/26/2024] [Indexed: 02/28/2024]
Abstract
Arabidopsis thaliana is currently the most-studied plant species on earth, with an unprecedented number of genetic, genomic, and molecular resources having been generated in this plant model. In the era of translating foundational discoveries to crops and beyond, we aimed to highlight the utility and challenges of using Arabidopsis as a reference for applied plant biology research, agricultural innovation, biotechnology, and medicine. We hope that this review will inspire the next generation of plant biologists to continue leveraging Arabidopsis as a robust and convenient experimental system to address fundamental and applied questions in biology. We aim to encourage lab and field scientists alike to take advantage of the vast Arabidopsis datasets, annotations, germplasm, constructs, methods, molecular and computational tools in our pursuit to advance understanding of plant biology and help feed the world's growing population. We envision that the power of Arabidopsis-inspired biotechnologies and foundational discoveries will continue to fuel the development of resilient, high-yielding, nutritious plants for the betterment of plant and animal health and greater environmental sustainability.
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Affiliation(s)
- Anna E Yaschenko
- Department of Plant and Microbial Biology, Genetics and Genomics Academy, North Carolina State University, Raleigh, NC 27695, USA
| | - Jose M Alonso
- Department of Plant and Microbial Biology, Genetics and Genomics Academy, North Carolina State University, Raleigh, NC 27695, USA
| | - Anna N Stepanova
- Department of Plant and Microbial Biology, Genetics and Genomics Academy, North Carolina State University, Raleigh, NC 27695, USA
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3
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Lacchini E, Erffelinck ML, Mertens J, Marcou S, Molina-Hidalgo FJ, Tzfadia O, Venegas-Molina J, Cárdenas PD, Pollier J, Tava A, Bak S, Höfte M, Goossens A. The saponin bomb: a nucleolar-localized β-glucosidase hydrolyzes triterpene saponins in Medicago truncatula. THE NEW PHYTOLOGIST 2023; 239:705-719. [PMID: 36683446 DOI: 10.1111/nph.18763] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 01/09/2023] [Indexed: 06/15/2023]
Abstract
Plants often protect themselves from their own bioactive defense metabolites by storing them in less active forms. Consequently, plants also need systems allowing correct spatiotemporal reactivation of such metabolites, for instance under pathogen or herbivore attack. Via co-expression analysis with public transcriptomes, we determined that the model legume Medicago truncatula has evolved a two-component system composed of a β-glucosidase, denominated G1, and triterpene saponins, which are physically separated from each other in intact cells. G1 expression is root-specific, stress-inducible, and coregulated with that of the genes encoding the triterpene saponin biosynthetic enzymes. However, the G1 protein is stored in the nucleolus and is released and united with its typically vacuolar-stored substrates only upon tissue damage, partly mediated by the surfactant action of the saponins themselves. Subsequently, enzymatic removal of carbohydrate groups from the saponins creates a pool of metabolites with an increased broad-spectrum antimicrobial activity. The evolution of this defense system benefited from both the intrinsic condensation abilities of the enzyme and the bioactivity properties of its substrates. We dub this two-component system the saponin bomb, in analogy with the mustard oil and cyanide bombs, commonly used to describe the renowned β-glucosidase-dependent defense systems for glucosinolates and cyanogenic glucosides.
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Affiliation(s)
- Elia Lacchini
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, B-9052, Belgium
- VIB Center for Plant Systems Biology, Ghent, B-9052, Belgium
| | - Marie-Laure Erffelinck
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, B-9052, Belgium
- VIB Center for Plant Systems Biology, Ghent, B-9052, Belgium
| | - Jan Mertens
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, B-9052, Belgium
- VIB Center for Plant Systems Biology, Ghent, B-9052, Belgium
| | - Shirley Marcou
- Department of Plants and Crops, Faculty of Bioscience Engineering, Ghent University, Ghent, B-9000, Belgium
| | - Francisco Javier Molina-Hidalgo
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, B-9052, Belgium
- VIB Center for Plant Systems Biology, Ghent, B-9052, Belgium
| | - Oren Tzfadia
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, B-9052, Belgium
- VIB Center for Plant Systems Biology, Ghent, B-9052, Belgium
| | - Jhon Venegas-Molina
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, B-9052, Belgium
- VIB Center for Plant Systems Biology, Ghent, B-9052, Belgium
| | - Pablo D Cárdenas
- Department of Plant and Environmental Sciences, University of Copenhagen, Frederiksberg C, DK-1871, Denmark
| | - Jacob Pollier
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, B-9052, Belgium
- VIB Center for Plant Systems Biology, Ghent, B-9052, Belgium
| | - Aldo Tava
- CREA Research Centre for Animal Production and Aquaculture, Lodi, 26900, Italy
| | - Søren Bak
- Department of Plant and Environmental Sciences, University of Copenhagen, Frederiksberg C, DK-1871, Denmark
| | - Monica Höfte
- Department of Plants and Crops, Faculty of Bioscience Engineering, Ghent University, Ghent, B-9000, Belgium
| | - Alain Goossens
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, B-9052, Belgium
- VIB Center for Plant Systems Biology, Ghent, B-9052, Belgium
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4
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Abstract
Rapid advances in genomic technologies have led to a wealth of diverse data, from which novel discoveries can be gleaned through the application of robust statistical and computational methods. Here, we describe GeneFishing, a semisupervised computational approach to reconstruct context-specific portraits of biological processes by leveraging gene-gene coexpression information. GeneFishing incorporates multiple high-dimensional statistical ideas, including dimensionality reduction, clustering, subsampling, and results aggregation, to produce robust results. To illustrate the power of our method, we applied it using 21 genes involved in cholesterol metabolism as "bait" to "fish out" (or identify) genes not previously identified as being connected to cholesterol metabolism. Using simulation and real datasets, we found that the results obtained through GeneFishing were more interesting for our study than those provided by related gene prioritization methods. In particular, application of GeneFishing to the GTEx liver RNA sequencing (RNAseq) data not only reidentified many known cholesterol-related genes, but also pointed to glyoxalase I (GLO1) as a gene implicated in cholesterol metabolism. In a follow-up experiment, we found that GLO1 knockdown in human hepatoma cell lines increased levels of cellular cholesterol ester, validating a role for GLO1 in cholesterol metabolism. In addition, we performed pantissue analysis by applying GeneFishing on various tissues and identified many potential tissue-specific cholesterol metabolism-related genes. GeneFishing appears to be a powerful tool for identifying related components of complex biological systems and may be used across a wide range of applications.
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Wurtzel ET. Changing Form and Function through Carotenoids and Synthetic Biology. PLANT PHYSIOLOGY 2019; 179:830-843. [PMID: 30361256 PMCID: PMC6393808 DOI: 10.1104/pp.18.01122] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Accepted: 10/06/2018] [Indexed: 05/06/2023]
Abstract
The diverse structures and multifaceted roles of carotenoids make these colorful pigments attractive targets for synthetic biology.
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Affiliation(s)
- Eleanore T Wurtzel
- Department of Biological Sciences, Lehman College, The City University of New York, Bronx, New York 10468
- The Graduate School and University Center-CUNY, New York, New York 10016-4309
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6
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Zwaenepoel A, Diels T, Amar D, Van Parys T, Shamir R, Van de Peer Y, Tzfadia O. MorphDB: Prioritizing Genes for Specialized Metabolism Pathways and Gene Ontology Categories in Plants. FRONTIERS IN PLANT SCIENCE 2018; 9:352. [PMID: 29616063 PMCID: PMC5867296 DOI: 10.3389/fpls.2018.00352] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Accepted: 03/02/2018] [Indexed: 05/20/2023]
Abstract
Recent times have seen an enormous growth of "omics" data, of which high-throughput gene expression data are arguably the most important from a functional perspective. Despite huge improvements in computational techniques for the functional classification of gene sequences, common similarity-based methods often fall short of providing full and reliable functional information. Recently, the combination of comparative genomics with approaches in functional genomics has received considerable interest for gene function analysis, leveraging both gene expression based guilt-by-association methods and annotation efforts in closely related model organisms. Besides the identification of missing genes in pathways, these methods also typically enable the discovery of biological regulators (i.e., transcription factors or signaling genes). A previously built guilt-by-association method is MORPH, which was proven to be an efficient algorithm that performs particularly well in identifying and prioritizing missing genes in plant metabolic pathways. Here, we present MorphDB, a resource where MORPH-based candidate genes for large-scale functional annotations (Gene Ontology, MapMan bins) are integrated across multiple plant species. Besides a gene centric query utility, we present a comparative network approach that enables researchers to efficiently browse MORPH predictions across functional gene sets and species, facilitating efficient gene discovery and candidate gene prioritization. MorphDB is available at http://bioinformatics.psb.ugent.be/webtools/morphdb/morphDB/index/. We also provide a toolkit, named "MORPH bulk" (https://github.com/arzwa/morph-bulk), for running MORPH in bulk mode on novel data sets, enabling researchers to apply MORPH to their own species of interest.
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Affiliation(s)
- Arthur Zwaenepoel
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
| | - Tim Diels
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
| | - David Amar
- Stanford Center for Inherited Cardiovascular Disease, Stanford University, Stanford, CA, United States
| | - Thomas Van Parys
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
| | - Ron Shamir
- Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel
| | - Yves Van de Peer
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
- Genomics Research Institute, University of Pretoria, Pretoria, South Africa
- *Correspondence: Yves Van de Peer
| | - Oren Tzfadia
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
- VIB Center for Plant Systems Biology, Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
- Oren Tzfadia
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7
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Labena AA, Gao YZ, Dong C, Hua HL, Guo FB. Metabolic pathway databases and model repositories. QUANTITATIVE BIOLOGY 2017. [DOI: 10.1007/s40484-017-0108-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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8
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Sibout R, Proost S, Hansen BO, Vaid N, Giorgi FM, Ho-Yue-Kuang S, Legée F, Cézart L, Bouchabké-Coussa O, Soulhat C, Provart N, Pasha A, Le Bris P, Roujol D, Hofte H, Jamet E, Lapierre C, Persson S, Mutwil M. Expression atlas and comparative coexpression network analyses reveal important genes involved in the formation of lignified cell wall in Brachypodium distachyon. THE NEW PHYTOLOGIST 2017; 215:1009-1025. [PMID: 28617955 DOI: 10.1111/nph.14635] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Accepted: 04/26/2017] [Indexed: 05/08/2023]
Abstract
While Brachypodium distachyon (Brachypodium) is an emerging model for grasses, no expression atlas or gene coexpression network is available. Such tools are of high importance to provide insights into the function of Brachypodium genes. We present a detailed Brachypodium expression atlas, capturing gene expression in its major organs at different developmental stages. The data were integrated into a large-scale coexpression database ( www.gene2function.de), enabling identification of duplicated pathways and conserved processes across 10 plant species, thus allowing genome-wide inference of gene function. We highlight the importance of the atlas and the platform through the identification of duplicated cell wall modules, and show that a lignin biosynthesis module is conserved across angiosperms. We identified and functionally characterised a putative ferulate 5-hydroxylase gene through overexpression of it in Brachypodium, which resulted in an increase in lignin syringyl units and reduced lignin content of mature stems, and led to improved saccharification of the stem biomass. Our Brachypodium expression atlas thus provides a powerful resource to reveal functionally related genes, which may advance our understanding of important biological processes in grasses.
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Affiliation(s)
- Richard Sibout
- Institut Jean-Pierre Bourgin, UMR 1318, INRA, AgroParisTech, CNRS, Université Paris-Saclay, RD10, Versailles Cedex, 78026, France
| | - Sebastian Proost
- Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam, 14476, Germany
| | - Bjoern Oest Hansen
- Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam, 14476, Germany
| | - Neha Vaid
- Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam, 14476, Germany
| | - Federico M Giorgi
- Cancer Research UK, Cambridge Institute, Robinson Way, Cambridge, CB2 0RE, UK
| | - Severine Ho-Yue-Kuang
- Institut Jean-Pierre Bourgin, UMR 1318, INRA, AgroParisTech, CNRS, Université Paris-Saclay, RD10, Versailles Cedex, 78026, France
| | - Frédéric Legée
- Institut Jean-Pierre Bourgin, UMR 1318, INRA, AgroParisTech, CNRS, Université Paris-Saclay, RD10, Versailles Cedex, 78026, France
| | - Laurent Cézart
- Institut Jean-Pierre Bourgin, UMR 1318, INRA, AgroParisTech, CNRS, Université Paris-Saclay, RD10, Versailles Cedex, 78026, France
| | - Oumaya Bouchabké-Coussa
- Institut Jean-Pierre Bourgin, UMR 1318, INRA, AgroParisTech, CNRS, Université Paris-Saclay, RD10, Versailles Cedex, 78026, France
| | - Camille Soulhat
- Institut Jean-Pierre Bourgin, UMR 1318, INRA, AgroParisTech, CNRS, Université Paris-Saclay, RD10, Versailles Cedex, 78026, France
| | - Nicholas Provart
- Department of Cell and Systems Biology, Centre for the Analysis of Genome Evolution and Function, University of Toronto, 25 Willcocks St., Toronto, ON, M5S 3B2, Canada
| | - Asher Pasha
- Department of Cell and Systems Biology, Centre for the Analysis of Genome Evolution and Function, University of Toronto, 25 Willcocks St., Toronto, ON, M5S 3B2, Canada
| | - Philippe Le Bris
- Institut Jean-Pierre Bourgin, UMR 1318, INRA, AgroParisTech, CNRS, Université Paris-Saclay, RD10, Versailles Cedex, 78026, France
| | - David Roujol
- Laboratoire de Recherche en Sciences Végétales, Université de Toulouse, CNRS, UPS, Castanet-Tolosan, France
| | - Herman Hofte
- Institut Jean-Pierre Bourgin, UMR 1318, INRA, AgroParisTech, CNRS, Université Paris-Saclay, RD10, Versailles Cedex, 78026, France
| | - Elisabeth Jamet
- Laboratoire de Recherche en Sciences Végétales, Université de Toulouse, CNRS, UPS, Castanet-Tolosan, France
| | - Catherine Lapierre
- Institut Jean-Pierre Bourgin, UMR 1318, INRA, AgroParisTech, CNRS, Université Paris-Saclay, RD10, Versailles Cedex, 78026, France
| | - Staffan Persson
- School of Biosciences, University of Melbourne, Parkville, Vic., 3010, Australia
| | - Marek Mutwil
- Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam, 14476, Germany
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9
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Ruprecht C, Vaid N, Proost S, Persson S, Mutwil M. Beyond Genomics: Studying Evolution with Gene Coexpression Networks. TRENDS IN PLANT SCIENCE 2017; 22:298-307. [PMID: 28126286 DOI: 10.1016/j.tplants.2016.12.011] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Revised: 12/06/2016] [Accepted: 12/22/2016] [Indexed: 05/08/2023]
Abstract
Understanding how genomes change as organisms become more complex is a central question in evolution. Molecular evolutionary studies typically correlate the appearance of genes and gene families with the emergence of biological pathways and morphological features. While such approaches are of great importance to understand how organisms evolve, they are also limited, as functionally related genes work together in contexts of dynamic gene networks. Since functionally related genes are often transcriptionally coregulated, gene coexpression networks present a resource to study the evolution of biological pathways. In this opinion article, we discuss recent developments in this field and how coexpression analyses can be merged with existing genomic approaches to transfer functional knowledge between species to study the appearance or extension of pathways.
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Affiliation(s)
- Colin Ruprecht
- Max-Planck Institute of Colloids and Interfaces, Am Muehlenberg 1, 14476 Potsdam, Germany
| | - Neha Vaid
- Max-Planck Institute for Molecular Plant Physiology, Am Muehlenberg 1, 14476 Potsdam, Germany
| | - Sebastian Proost
- Max-Planck Institute for Molecular Plant Physiology, Am Muehlenberg 1, 14476 Potsdam, Germany
| | - Staffan Persson
- School of BioSciences, University of Melbourne, Parkville, VIC 3010, Australia; ARC Centre of Excellence in Plant Cell Walls, School of Biosciences, University of Melbourne,Parkville, VIC 3010, Australia
| | - Marek Mutwil
- Max-Planck Institute for Molecular Plant Physiology, Am Muehlenberg 1, 14476 Potsdam, Germany.
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Abstract
Functional relations between genes can be represented as networks. These networks have been successfully used to infer gene function and to mediate transfer of functional knowledge between species. Transcriptionally coordinated or co-expressed genes tend to be functionally related, which combined with availability of transcriptomic data for multiple plant species make the co-expression networks a useful resource for the plant community. In this chapter, we describe PlaNet ( www.gene2function.de ), a database that includes comparative analyses for co-expression networks of 11 plant species. We exemplify how the tools included in PlaNet can be used to predict gene function, transfer knowledge, and discover conserved and multiplied gene modules.
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Affiliation(s)
- Sebastian Proost
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany
| | - Marek Mutwil
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany.
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11
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Missing gene identification using functional coherence scores. Sci Rep 2016; 6:31725. [PMID: 27552989 PMCID: PMC4995438 DOI: 10.1038/srep31725] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2016] [Accepted: 07/22/2016] [Indexed: 11/18/2022] Open
Abstract
Reconstructing metabolic and signaling pathways is an effective way of interpreting a genome sequence. A challenge in a pathway reconstruction is that often genes in a pathway cannot be easily found, reflecting current imperfect information of the target organism. In this work, we developed a new method for finding missing genes, which integrates multiple features, including gene expression, phylogenetic profile, and function association scores. Particularly, for considering function association between candidate genes and neighboring proteins to the target missing gene in the network, we used Co-occurrence Association Score (CAS) and PubMed Association Score (PAS), which are designed for capturing functional coherence of proteins. We showed that adding CAS and PAS substantially improve the accuracy of identifying missing genes in the yeast enzyme-enzyme network compared to the cases when only the conventional features, gene expression, phylogenetic profile, were used. Finally, it was also demonstrated that the accuracy improves by considering indirect neighbors to the target enzyme position in the network using a proper network-topology-based weighting scheme.
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12
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Ruprecht C, Mendrinna A, Tohge T, Sampathkumar A, Klie S, Fernie AR, Nikoloski Z, Persson S, Mutwil M. FamNet: A Framework to Identify Multiplied Modules Driving Pathway Expansion in Plants. PLANT PHYSIOLOGY 2016; 170:1878-94. [PMID: 26754669 PMCID: PMC4775111 DOI: 10.1104/pp.15.01281] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2015] [Accepted: 01/07/2016] [Indexed: 05/07/2023]
Abstract
Gene duplications generate new genes that can acquire similar but often diversified functions. Recent studies of gene coexpression networks have indicated that, not only genes, but also pathways can be multiplied and diversified to perform related functions in different parts of an organism. Identification of such diversified pathways, or modules, is needed to expand our knowledge of biological processes in plants and to understand how biological functions evolve. However, systematic explorations of modules remain scarce, and no user-friendly platform to identify them exists. We have established a statistical framework to identify modules and show that approximately one-third of the genes of a plant's genome participate in hundreds of multiplied modules. Using this framework as a basis, we implemented a platform that can explore and visualize multiplied modules in coexpression networks of eight plant species. To validate the usefulness of the platform, we identified and functionally characterized pollen- and root-specific cell wall modules that multiplied to confer tip growth in pollen tubes and root hairs, respectively. Furthermore, we identified multiplied modules involved in secondary metabolite synthesis and corroborated them by metabolite profiling of tobacco (Nicotiana tabacum) tissues. The interactive platform, referred to as FamNet, is available at http://www.gene2function.de/famnet.html.
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Affiliation(s)
- Colin Ruprecht
- Max Planck Institute for Molecular Plant Physiology, 14476 Potsdam, Germany (C.R., T.T, S.K., A.R.F., Z.N., M.M.), School of Biosciences and Australian Research Council Centre of Excellence in Plant Cell Walls, University of Melbourne, Parkville, Victoria 3010, Australia (A.M., S.P.); andDivision of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125 (A.S.)
| | - Amelie Mendrinna
- Max Planck Institute for Molecular Plant Physiology, 14476 Potsdam, Germany (C.R., T.T, S.K., A.R.F., Z.N., M.M.), School of Biosciences and Australian Research Council Centre of Excellence in Plant Cell Walls, University of Melbourne, Parkville, Victoria 3010, Australia (A.M., S.P.); andDivision of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125 (A.S.)
| | - Takayuki Tohge
- Max Planck Institute for Molecular Plant Physiology, 14476 Potsdam, Germany (C.R., T.T, S.K., A.R.F., Z.N., M.M.), School of Biosciences and Australian Research Council Centre of Excellence in Plant Cell Walls, University of Melbourne, Parkville, Victoria 3010, Australia (A.M., S.P.); andDivision of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125 (A.S.)
| | - Arun Sampathkumar
- Max Planck Institute for Molecular Plant Physiology, 14476 Potsdam, Germany (C.R., T.T, S.K., A.R.F., Z.N., M.M.), School of Biosciences and Australian Research Council Centre of Excellence in Plant Cell Walls, University of Melbourne, Parkville, Victoria 3010, Australia (A.M., S.P.); andDivision of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125 (A.S.)
| | - Sebastian Klie
- Max Planck Institute for Molecular Plant Physiology, 14476 Potsdam, Germany (C.R., T.T, S.K., A.R.F., Z.N., M.M.), School of Biosciences and Australian Research Council Centre of Excellence in Plant Cell Walls, University of Melbourne, Parkville, Victoria 3010, Australia (A.M., S.P.); andDivision of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125 (A.S.)
| | - Alisdair R Fernie
- Max Planck Institute for Molecular Plant Physiology, 14476 Potsdam, Germany (C.R., T.T, S.K., A.R.F., Z.N., M.M.), School of Biosciences and Australian Research Council Centre of Excellence in Plant Cell Walls, University of Melbourne, Parkville, Victoria 3010, Australia (A.M., S.P.); andDivision of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125 (A.S.)
| | - Zoran Nikoloski
- Max Planck Institute for Molecular Plant Physiology, 14476 Potsdam, Germany (C.R., T.T, S.K., A.R.F., Z.N., M.M.), School of Biosciences and Australian Research Council Centre of Excellence in Plant Cell Walls, University of Melbourne, Parkville, Victoria 3010, Australia (A.M., S.P.); andDivision of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125 (A.S.)
| | - Staffan Persson
- Max Planck Institute for Molecular Plant Physiology, 14476 Potsdam, Germany (C.R., T.T, S.K., A.R.F., Z.N., M.M.), School of Biosciences and Australian Research Council Centre of Excellence in Plant Cell Walls, University of Melbourne, Parkville, Victoria 3010, Australia (A.M., S.P.); andDivision of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125 (A.S.)
| | - Marek Mutwil
- Max Planck Institute for Molecular Plant Physiology, 14476 Potsdam, Germany (C.R., T.T, S.K., A.R.F., Z.N., M.M.), School of Biosciences and Australian Research Council Centre of Excellence in Plant Cell Walls, University of Melbourne, Parkville, Victoria 3010, Australia (A.M., S.P.); andDivision of Biology and Biological Engineering, California Institute of Technology, Pasadena, California 91125 (A.S.)
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13
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Tzfadia O, Diels T, De Meyer S, Vandepoele K, Aharoni A, Van de Peer Y. CoExpNetViz: Comparative Co-Expression Networks Construction and Visualization Tool. FRONTIERS IN PLANT SCIENCE 2016; 6:1194. [PMID: 26779228 PMCID: PMC4700130 DOI: 10.3389/fpls.2015.01194] [Citation(s) in RCA: 64] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Accepted: 12/11/2015] [Indexed: 05/23/2023]
Abstract
MOTIVATION Comparative transcriptomics is a common approach in functional gene discovery efforts. It allows for finding conserved co-expression patterns between orthologous genes in closely related plant species, suggesting that these genes potentially share similar function and regulation. Several efficient co-expression-based tools have been commonly used in plant research but most of these pipelines are limited to data from model systems, which greatly limit their utility. Moreover, in addition, none of the existing pipelines allow plant researchers to make use of their own unpublished gene expression data for performing a comparative co-expression analysis and generate multi-species co-expression networks. RESULTS We introduce CoExpNetViz, a computational tool that uses a set of query or "bait" genes as an input (chosen by the user) and a minimum of one pre-processed gene expression dataset. The CoExpNetViz algorithm proceeds in three main steps; (i) for every bait gene submitted, co-expression values are calculated using mutual information and Pearson correlation coefficients, (ii) non-bait (or target) genes are grouped based on cross-species orthology, and (iii) output files are generated and results can be visualized as network graphs in Cytoscape. AVAILABILITY The CoExpNetViz tool is freely available both as a PHP web server (link: http://bioinformatics.psb.ugent.be/webtools/coexpr/) (implemented in C++) and as a Cytoscape plugin (implemented in Java). Both versions of the CoExpNetViz tool support LINUX and Windows platforms.
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Affiliation(s)
- Oren Tzfadia
- Department of Plant Systems Biology, Vlaams Instituut voor BiotechnologieGhent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent UniversityGhent, Belgium
- Bioinformatics Institute Ghent, Ghent UniversityGhent, Belgium
| | - Tim Diels
- Department of Plant Systems Biology, Vlaams Instituut voor BiotechnologieGhent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent UniversityGhent, Belgium
- Bioinformatics Institute Ghent, Ghent UniversityGhent, Belgium
| | - Sam De Meyer
- Department of Plant Systems Biology, Vlaams Instituut voor BiotechnologieGhent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent UniversityGhent, Belgium
| | - Klaas Vandepoele
- Department of Plant Systems Biology, Vlaams Instituut voor BiotechnologieGhent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent UniversityGhent, Belgium
- Bioinformatics Institute Ghent, Ghent UniversityGhent, Belgium
| | - Asaph Aharoni
- Department of Plant Sciences and the Environment, Weizmann Institute of ScienceRehovot, Israel
| | - Yves Van de Peer
- Department of Plant Systems Biology, Vlaams Instituut voor BiotechnologieGhent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent UniversityGhent, Belgium
- Bioinformatics Institute Ghent, Ghent UniversityGhent, Belgium
- Genomics Research Institute, University of PretoriaPretoria, South Africa
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14
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Serin EAR, Nijveen H, Hilhorst HWM, Ligterink W. Learning from Co-expression Networks: Possibilities and Challenges. FRONTIERS IN PLANT SCIENCE 2016; 7:444. [PMID: 27092161 PMCID: PMC4825623 DOI: 10.3389/fpls.2016.00444] [Citation(s) in RCA: 186] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2016] [Accepted: 03/21/2016] [Indexed: 05/18/2023]
Abstract
Plants are fascinating and complex organisms. A comprehensive understanding of the organization, function and evolution of plant genes is essential to disentangle important biological processes and to advance crop engineering and breeding strategies. The ultimate aim in deciphering complex biological processes is the discovery of causal genes and regulatory mechanisms controlling these processes. The recent surge of omics data has opened the door to a system-wide understanding of the flow of biological information underlying complex traits. However, dealing with the corresponding large data sets represents a challenging endeavor that calls for the development of powerful bioinformatics methods. A popular approach is the construction and analysis of gene networks. Such networks are often used for genome-wide representation of the complex functional organization of biological systems. Network based on similarity in gene expression are called (gene) co-expression networks. One of the major application of gene co-expression networks is the functional annotation of unknown genes. Constructing co-expression networks is generally straightforward. In contrast, the resulting network of connected genes can become very complex, which limits its biological interpretation. Several strategies can be employed to enhance the interpretation of the networks. A strategy in coherence with the biological question addressed needs to be established to infer reliable networks. Additional benefits can be gained from network-based strategies using prior knowledge and data integration to further enhance the elucidation of gene regulatory relationships. As a result, biological networks provide many more applications beyond the simple visualization of co-expressed genes. In this study we review the different approaches for co-expression network inference in plants. We analyse integrative genomics strategies used in recent studies that successfully identified candidate genes taking advantage of gene co-expression networks. Additionally, we discuss promising bioinformatics approaches that predict networks for specific purposes.
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Affiliation(s)
- Elise A. R. Serin
- Wageningen Seed Lab, Laboratory of Plant Physiology, Wageningen UniversityWageningen, Netherlands
| | - Harm Nijveen
- Wageningen Seed Lab, Laboratory of Plant Physiology, Wageningen UniversityWageningen, Netherlands
- Laboratory of Bioinformatics, Wageningen UniversityWageningen, Netherlands
| | - Henk W. M. Hilhorst
- Wageningen Seed Lab, Laboratory of Plant Physiology, Wageningen UniversityWageningen, Netherlands
| | - Wilco Ligterink
- Wageningen Seed Lab, Laboratory of Plant Physiology, Wageningen UniversityWageningen, Netherlands
- *Correspondence: Wilco Ligterink
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15
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Amar D, Frades I, Diels T, Zaltzman D, Ghatan N, Hedley PE, Alexandersson E, Tzfadia O, Shamir R. The MORPH-R web server and software tool for predicting missing genes in biological pathways. PHYSIOLOGIA PLANTARUM 2015; 155:12-20. [PMID: 25625434 DOI: 10.1111/ppl.12326] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2014] [Accepted: 01/12/2015] [Indexed: 06/04/2023]
Abstract
A biological pathway is the set of molecular entities involved in a given biological process and the interrelations among them. Even though biological pathways have been studied extensively, discovering missing genes in pathways remains a fundamental challenge. Here, we present an easy-to-use tool that allows users to run MORPH (MOdule-guided Ranking of candidate PatHway genes), an algorithm for revealing missing genes in biological pathways, and demonstrate its capabilities. MORPH supports the analysis in tomato, Arabidopsis and the two new species: rice and the newly sequenced potato genome. The new tool, called MORPH-R, is available both as a web server (at http://bioinformatics.psb.ugent.be/webtools/morph/) and as standalone software that can be used locally. In the standalone version, the user can apply the tool to new organisms using any proprietary and public data sources.
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Affiliation(s)
- David Amar
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Itziar Frades
- Department of Plant Protection Biology, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - Tim Diels
- Department of Plant Systems Biology, VIB, Ghent, Belgium
- Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
| | - David Zaltzman
- Department of Plant Science, The Weizmann Institute of Science, Rehovot, Israel
| | - Netanel Ghatan
- Department of Plant Science, The Weizmann Institute of Science, Rehovot, Israel
| | - Pete E Hedley
- Cell and Molecular Sciences, The James Hutton Institute, Dundee, United Kingdom
| | - Erik Alexandersson
- Department of Plant Protection Biology, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - Oren Tzfadia
- Department of Plant Systems Biology, VIB, Ghent, Belgium
- Department of Plant Science, The Weizmann Institute of Science, Rehovot, Israel
| | - Ron Shamir
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
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16
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Ransbotyn V, Yeger-Lotem E, Basha O, Acuna T, Verduyn C, Gordon M, Chalifa-Caspi V, Hannah MA, Barak S. A combination of gene expression ranking and co-expression network analysis increases discovery rate in large-scale mutant screens for novel Arabidopsis thaliana abiotic stress genes. PLANT BIOTECHNOLOGY JOURNAL 2015; 13:501-13. [PMID: 25370817 DOI: 10.1111/pbi.12274] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2014] [Revised: 07/29/2014] [Accepted: 08/28/2014] [Indexed: 05/20/2023]
Abstract
As challenges to food security increase, the demand for lead genes for improving crop production is growing. However, genetic screens of plant mutants typically yield very low frequencies of desired phenotypes. Here, we present a powerful computational approach for selecting candidate genes for screening insertion mutants. We combined ranking of Arabidopsis thaliana regulatory genes according to their expression in response to multiple abiotic stresses (Multiple Stress [MST] score), with stress-responsive RNA co-expression network analysis to select candidate multiple stress regulatory (MSTR) genes. Screening of 62 T-DNA insertion mutants defective in candidate MSTR genes, for abiotic stress germination phenotypes yielded a remarkable hit rate of up to 62%; this gene discovery rate is 48-fold greater than that of other large-scale insertional mutant screens. Moreover, the MST score of these genes could be used to prioritize them for screening. To evaluate the contribution of the co-expression analysis, we screened 64 additional mutant lines of MST-scored genes that did not appear in the RNA co-expression network. The screening of these MST-scored genes yielded a gene discovery rate of 36%, which is much higher than that of classic mutant screens but not as high as when picking candidate genes from the co-expression network. The MSTR co-expression network that we created, AraSTressRegNet is publicly available at http://netbio.bgu.ac.il/arnet. This systems biology-based screening approach combining gene ranking and network analysis could be generally applicable to enhancing identification of genes regulating additional processes in plants and other organisms provided that suitable transcriptome data are available.
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Affiliation(s)
- Vanessa Ransbotyn
- French Associates Institute for Agriculture and Biotechnology of Drylands, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Midreshet Ben-Gurion, Israel
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17
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Dong X, Jiang Z, Peng YL, Zhang Z. Revealing shared and distinct gene network organization in Arabidopsis immune responses by integrative analysis. PLANT PHYSIOLOGY 2015; 167:1186-203. [PMID: 25614062 PMCID: PMC4348776 DOI: 10.1104/pp.114.254292] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) are two main plant immune responses to counter pathogen invasion. Genome-wide gene network organizing principles leading to quantitative differences between PTI and ETI have remained elusive. We combined an advanced machine learning method and modular network analysis to systematically characterize the organizing principles of Arabidopsis (Arabidopsis thaliana) PTI and ETI at three network resolutions. At the single network node/edge level, we ranked genes and gene interactions based on their ability to distinguish immune response from normal growth and successfully identified many immune-related genes associated with PTI and ETI. Topological analysis revealed that the top-ranked gene interactions tend to link network modules. At the subnetwork level, we identified a subnetwork shared by PTI and ETI encompassing 1,159 genes and 1,289 interactions. This subnetwork is enriched in interactions linking network modules and is also a hotspot of attack by pathogen effectors. The subnetwork likely represents a core component in the coordination of multiple biological processes to favor defense over development. Finally, we constructed modular network models for PTI and ETI to explain the quantitative differences in the global network architecture. Our results indicate that the defense modules in ETI are organized into relatively independent structures, explaining the robustness of ETI to genetic mutations and effector attacks. Taken together, the multiscale comparisons of PTI and ETI provide a systems biology perspective on plant immunity and emphasize coordination among network modules to establish a robust immune response.
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Affiliation(s)
- Xiaobao Dong
- State Key Laboratory of Agrobiotechnology (X.D., Z.J., Y.-L.P., Z.Z.), College of Biological Sciences (X.D., Z.J., Z.Z.), and Ministry of Agriculture Key Laboratory for Plant Pathology (Y.-L.P.), China Agricultural University, Beijing 100193, China
| | - Zhenhong Jiang
- State Key Laboratory of Agrobiotechnology (X.D., Z.J., Y.-L.P., Z.Z.), College of Biological Sciences (X.D., Z.J., Z.Z.), and Ministry of Agriculture Key Laboratory for Plant Pathology (Y.-L.P.), China Agricultural University, Beijing 100193, China
| | - You-Liang Peng
- State Key Laboratory of Agrobiotechnology (X.D., Z.J., Y.-L.P., Z.Z.), College of Biological Sciences (X.D., Z.J., Z.Z.), and Ministry of Agriculture Key Laboratory for Plant Pathology (Y.-L.P.), China Agricultural University, Beijing 100193, China
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology (X.D., Z.J., Y.-L.P., Z.Z.), College of Biological Sciences (X.D., Z.J., Z.Z.), and Ministry of Agriculture Key Laboratory for Plant Pathology (Y.-L.P.), China Agricultural University, Beijing 100193, China
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18
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Amar D, Frades I, Danek A, Goldberg T, Sharma SK, Hedley PE, Proux-Wera E, Andreasson E, Shamir R, Tzfadia O, Alexandersson E. Evaluation and integration of functional annotation pipelines for newly sequenced organisms: the potato genome as a test case. BMC PLANT BIOLOGY 2014; 14:329. [PMID: 25476999 PMCID: PMC4274702 DOI: 10.1186/s12870-014-0329-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2014] [Accepted: 11/10/2014] [Indexed: 05/08/2023]
Abstract
BACKGROUND For most organisms, even if their genome sequence is available, little functional information about individual genes or proteins exists. Several annotation pipelines have been developed for functional analysis based on sequence, 'omics', and literature data. However, researchers encounter little guidance on how well they perform. Here, we used the recently sequenced potato genome as a case study. The potato genome was selected since its genome is newly sequenced and it is a non-model plant even if there is relatively ample information on individual potato genes, and multiple gene expression profiles are available. RESULTS We show that the automatic gene annotations of potato have low accuracy when compared to a "gold standard" based on experimentally validated potato genes. Furthermore, we evaluate six state-of-the-art annotation pipelines and show that their predictions are markedly dissimilar (Jaccard similarity coefficient of 0.27 between pipelines on average). To overcome this discrepancy, we introduce a simple GO structure-based algorithm that reconciles the predictions of the different pipelines. We show that the integrated annotation covers more genes, increases by over 50% the number of highly co-expressed GO processes, and obtains much higher agreement with the gold standard. CONCLUSIONS We find that different annotation pipelines produce different results, and show how to integrate them into a unified annotation that is of higher quality than each single pipeline. We offer an improved functional annotation of both PGSC and ITAG potato gene models, as well as tools that can be applied to additional pipelines and improve annotation in other organisms. This will greatly aid future functional analysis of '-omics' datasets from potato and other organisms with newly sequenced genomes. The new potato annotations are available with this paper.
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Affiliation(s)
- David Amar
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
| | - Itziar Frades
- Deptartment of Plant Protection Biology, Swedish University of Agricultural Sciences, Alnarp, Sweden.
| | - Agnieszka Danek
- Institute of Informatics, Silesian University of Technology, Akademicka 2A, 44-100, Gliwice, Poland.
| | - Tatyana Goldberg
- Department for Bioinformatics and Computational Biology, Technical University of Munich, Arcisstraße 21, 80333, Munich, Germany.
| | - Sanjeev K Sharma
- Cell and Molecular Sciences, The James Hutton Institute, Aberdeen, Scotland, UK.
| | - Pete E Hedley
- Cell and Molecular Sciences, The James Hutton Institute, Aberdeen, Scotland, UK.
| | - Estelle Proux-Wera
- Deptartment of Plant Protection Biology, Swedish University of Agricultural Sciences, Alnarp, Sweden.
- Current affiliation: SciLifeLab, Stockholm University, Universitetsvägen 10, 114 18, Stockholm, Sweden.
| | - Erik Andreasson
- Deptartment of Plant Protection Biology, Swedish University of Agricultural Sciences, Alnarp, Sweden.
| | - Ron Shamir
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
| | - Oren Tzfadia
- Department of Plant Science, The Weizmann Institute of Science, Rehovot, Israel.
| | - Erik Alexandersson
- Deptartment of Plant Protection Biology, Swedish University of Agricultural Sciences, Alnarp, Sweden.
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19
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Amar D, Shamir R. Constructing module maps for integrated analysis of heterogeneous biological networks. Nucleic Acids Res 2014; 42:4208-19. [PMID: 24497192 PMCID: PMC3985673 DOI: 10.1093/nar/gku102] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Improved methods for integrated analysis of heterogeneous large-scale omic data are direly needed. Here, we take a network-based approach to this challenge. Given two networks, representing different types of gene interactions, we construct a map of linked modules, where modules are genes strongly connected in the first network and links represent strong inter-module connections in the second. We develop novel algorithms that considerably outperform prior art on simulated and real data from three distinct domains. First, by analyzing protein-protein interactions and negative genetic interactions in yeast, we discover epistatic relations among protein complexes. Second, we analyze protein-protein interactions and DNA damage-specific positive genetic interactions in yeast and reveal functional rewiring among protein complexes, suggesting novel mechanisms of DNA damage response. Finally, using transcriptomes of non-small-cell lung cancer patients, we analyze networks of global co-expression and disease-dependent differential co-expression and identify a sharp drop in correlation between two modules of immune activation processes, with possible microRNA control. Our study demonstrates that module maps are a powerful tool for deeper analysis of heterogeneous high-throughput omic data.
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Affiliation(s)
- David Amar
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel
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