23
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Connectivity in gene coexpression networks negatively correlates with rates of molecular evolution in flowering plants

      research-article
      1 , * , 2 , 1
      PLoS ONE
      Public Library of Science

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Gene coexpression networks are a useful tool for summarizing transcriptomic data and providing insight into patterns of gene regulation in a variety of species. Though there has been considerable interest in studying the evolution of network topology across species, less attention has been paid to the relationship between network position and patterns of molecular evolution. Here, we generated coexpression networks from publicly available expression data for seven flowering plant taxa ( Arabidopsis thaliana, Glycine max, Oryza sativa, Populus spp., Solanum lycopersicum, Vitis spp., and Zea mays) to investigate the relationship between network position and rates of molecular evolution. We found a significant negative correlation between network connectivity and rates of molecular evolution, with more highly connected (i.e., “hub”) genes having significantly lower nonsynonymous substitution rates and dN/ dS ratios compared to less highly connected (i.e., “peripheral”) genes across the taxa surveyed. These findings suggest that more centrally located hub genes are, on average, subject to higher levels of evolutionary constraint than are genes located on the periphery of gene coexpression networks. The consistency of this result across disparate taxa suggests that it holds for flowering plants in general, as opposed to being a species-specific phenomenon.

          Related collections

          Most cited references45

          • Record: found
          • Abstract: found
          • Article: not found

          A gene-coexpression network for global discovery of conserved genetic modules.

          To elucidate gene function on a global scale, we identified pairs of genes that are coexpressed over 3182 DNA microarrays from humans, flies, worms, and yeast. We found 22,163 such coexpression relationships, each of which has been conserved across evolution. This conservation implies that the coexpression of these gene pairs confers a selective advantage and therefore that these genes are functionally related. Many of these relationships provide strong evidence for the involvement of new genes in core biological functions such as the cell cycle, secretion, and protein expression. We experimentally confirmed the predictions implied by some of these links and identified cell proliferation functions for several genes. By assembling these links into a gene-coexpression network, we found several components that were animal-specific as well as interrelationships between newly evolved and ancient modules.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Coexpression analysis of human genes across many microarray data sets.

            We present a large-scale analysis of mRNA coexpression based on 60 large human data sets containing a total of 3924 microarrays. We sought pairs of genes that were reliably coexpressed (based on the correlation of their expression profiles) in multiple data sets, establishing a high-confidence network of 8805 genes connected by 220,649 "coexpression links" that are observed in at least three data sets. Confirmed positive correlations between genes were much more common than confirmed negative correlations. We show that confirmation of coexpression in multiple data sets is correlated with functional relatedness, and show how cluster analysis of the network can reveal functionally coherent groups of genes. Our findings demonstrate how the large body of accumulated microarray data can be exploited to increase the reliability of inferences about gene function. Copyright 2004 Cold Spring Harbor Laboratory Press
              Bookmark
              • Record: found
              • Abstract: not found
              • Book: not found

              Conspectus florae Graecae / auctore E. de Halácsy.

                Bookmark

                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: ValidationRole: VisualizationRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: VisualizationRole: Writing – original draft
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Project administrationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                31 July 2017
                2017
                : 12
                : 7
                : e0182289
                Affiliations
                [1 ] Department of Plant Biology, University of Georgia, Athens, Georgia, United States of America
                [2 ] Department of Genetics, University of Georgia, Athens, Georgia, United States of America
                University of Toronto, CANADA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-3661-690X
                Article
                PONE-D-17-15415
                10.1371/journal.pone.0182289
                5536297
                28759647
                e134a20b-97ac-4168-8d99-5f4c44bdd91d
                © 2017 Masalia et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 20 April 2017
                : 14 July 2017
                Page count
                Figures: 2, Tables: 0, Pages: 10
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000076, Directorate for Biological Sciences;
                Award ID: DBI-1444522
                Award Recipient :
                This study was funded by a grant from the National Science Foundation Plant Genome Research Program (DBI-1444522; https://nsf.gov/funding/pgm_summ.jsp?pims_id=5338) received by John M. Burke. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Genetics
                Gene Identification and Analysis
                Genetic Networks
                Computer and Information Sciences
                Network Analysis
                Genetic Networks
                Biology and Life Sciences
                Evolutionary Biology
                Molecular Evolution
                Research and Analysis Methods
                Experimental Organism Systems
                Model Organisms
                Arabidopsis Thaliana
                Research and Analysis Methods
                Model Organisms
                Arabidopsis Thaliana
                Biology and Life Sciences
                Organisms
                Plants
                Brassica
                Arabidopsis Thaliana
                Research and Analysis Methods
                Experimental Organism Systems
                Plant and Algal Models
                Arabidopsis Thaliana
                Biology and Life Sciences
                Organisms
                Plants
                Flowering Plants
                Computer and Information Sciences
                Network Analysis
                Protein Interaction Networks
                Biology and Life Sciences
                Biochemistry
                Proteomics
                Protein Interaction Networks
                Biology and Life Sciences
                Evolutionary Biology
                Evolutionary Genetics
                Biology and Life Sciences
                Organisms
                Plants
                Flowering Plants
                Grapevine
                Research and Analysis Methods
                Experimental Organism Systems
                Model Organisms
                Maize
                Research and Analysis Methods
                Model Organisms
                Maize
                Biology and Life Sciences
                Organisms
                Plants
                Grasses
                Maize
                Research and Analysis Methods
                Experimental Organism Systems
                Plant and Algal Models
                Maize
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

                Uncategorized
                Uncategorized

                Comments

                Comment on this article