Evolution of Spatially Coexpressed Families of Type-2 Vomeronasal Receptors in Rodents – ScienceOpen
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      Evolution of Spatially Coexpressed Families of Type-2 Vomeronasal Receptors in Rodents

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          Abstract

          The vomeronasal organ (VNO) is an olfactory structure for the detection of pheromones. VNO neurons express three groups of unrelated G-protein-coupled receptors. Type-2 vomeronasal receptors (V2Rs) are specifically localized in the basal neurons of the VNO and are believed to sense protein pheromones eliciting specific reproductive behaviors. In murine species, V2Rs are organized into four families. Family-ABD V2Rs are expressed monogenically and coexpress with family-C V2Rs of either subfamily C1 (V2RC1) or subfamily C2 (V2RC2), according to a coordinate temporal diagram. Neurons expressing the phylogenetically ancient V2RC1 coexpress family-BD V2Rs or a specific group of subfamily-A V2Rs (V2RA8-10), whereas a second neuronal subset (V2RC2-positive) coexpresses a recently expanded group of five subfamily-A V2Rs (V2RA1-5) along with vomeronasal-specific Major Histocompatibility Complex molecules (H2-Mv). Through database mining and Sanger sequencing, we have analyzed the onset, diversification, and expansion of the V2R-families throughout the phylogeny of Rodentia. Our results suggest that the separation of V2RC1 and V2RC2 occurred in a Cricetidae ancestor in coincidence with the evolution of the H2-Mv genes; this phylogenetic event did not correspond with the origin of the coexpressing V2RA1-5 genes, which dates back to an ancestral myomorphan lineage. Interestingly, the evolution of receptors within the V2RA1-5 group may be implicated in the origin and diversification of some of the V2R putative cognate ligands, the exocrine secreting peptides. The establishment of V2RC2, which probably reflects the complex expansion and diversification of family-A V2Rs, generated receptors that have probably acquired a more subtle functional specificity.

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          Profile hidden Markov models.

          S. Eddy (1998)
          The recent literature on profile hidden Markov model (profile HMM) methods and software is reviewed. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system suitable for searching databases for remotely homologous sequences. Profile HMM analyses complement standard pairwise comparison methods for large-scale sequence analysis. Several software implementations and two large libraries of profile HMMs of common protein domains are available. HMM methods performed comparably to threading methods in the CASP2 structure prediction exercise.
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            TimeTree: a public knowledge-base of divergence times among organisms.

            Biologists and other scientists routinely need to know times of divergence between species and to construct phylogenies calibrated to time (timetrees). Published studies reporting time estimates from molecular data have been increasing rapidly, but the data have been largely inaccessible to the greater community of scientists because of their complexity. TimeTree brings these data together in a consistent format and uses a hierarchical structure, corresponding to the tree of life, to maximize their utility. Results are presented and summarized, allowing users to quickly determine the range and robustness of time estimates and the degree of consensus from the published literature. TimeTree is available at http://www.timetree.net
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              Estimating maximum likelihood phylogenies with PhyML.

              Our understanding of the origins, the functions and/or the structures of biological sequences strongly depends on our ability to decipher the mechanisms of molecular evolution. These complex processes can be described through the comparison of homologous sequences in a phylogenetic framework. Moreover, phylogenetic inference provides sound statistical tools to exhibit the main features of molecular evolution from the analysis of actual sequences. This chapter focuses on phylogenetic tree estimation under the maximum likelihood (ML) principle. Phylogenies inferred under this probabilistic criterion are usually reliable and important biological hypotheses can be tested through the comparison of different models. Estimating ML phylogenies is computationally demanding, and careful examination of the results is warranted. This chapter focuses on PhyML, a software that implements recent ML phylogenetic methods and algorithms. We illustrate the strengths and pitfalls of this program through the analysis of a real data set. PhyML v3.0 is available from (http://atgc_montpellier.fr/phyml/).
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                Author and article information

                Journal
                Genome Biol Evol
                Genome Biol Evol
                gbe
                gbe
                Genome Biology and Evolution
                Oxford University Press
                1759-6653
                January 2015
                23 December 2014
                23 December 2014
                : 7
                : 1
                : 272-285
                Affiliations
                1Department of Neuroscience, University of Parma, Italy
                2Laboratoire de Paleontologie, Institut des Sciences de l’Evolution, UMR 5554 Centre National de la Recherche Scientifique, Université de Montpellier 2, France
                3Department of Life Sciences, University of Parma, Italy
                Author notes
                *Corresponding author: E-mail: robertin@ 123456unipr.it .

                Associate editor: Ya-Ping Zhang

                Article
                evu283
                10.1093/gbe/evu283
                4316634
                25539725
                3aeeef81-0db3-4b9f-8d4d-68b0ed5543e0
                © The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 18 December 2014
                Page count
                Pages: 14
                Categories
                Research Article

                Genetics
                vomeronasal,pheromones,chemosensory,evolution,phylogeny,rodents
                Genetics
                vomeronasal, pheromones, chemosensory, evolution, phylogeny, rodents

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