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An Empirical Analysis of mt 16S rRNA Covarion-Like Evolution in Insects: Site-Specific Rate Variation Is Clustered and Frequently Detected
Authors:B Misof  CL Anderson  TR Buckley  D Erpenbeck  A Rickert  K Misof
Institution:(1) Department of Entomology, Zoological Research Institute and Museum Alexander Koenig, Adenauerallee 160, Bonn, Germany, DE;(2) Laboratory for Ecological and Conservation Genetics, College of Natural Resources, University of Idaho, Moscow, ID, USA, US;(3) Department of Biology, Duke University, Durham, NC, USA, US;(4) Institute for Biodiversity and Ecosystem Dynamics (Zoological Museum), University of Amsterdam, Amsterdam, The Netherlands, NL;(5) Max-Planck-Institute for Breeding Research, Cologne, Germany, DE;(6) Erich-Schmid-Institut, Leoben, Austria, AT
Abstract:The structural and functional analysis of rRNA molecules has attracted considerable scientific interest. Empirical studies have demonstrated that sequence variation is not directly translated into modifications of rRNA secondary structure. Obviously, the maintenance of secondary structure and sequence variation are in part governed by different selection regimes. The nature of those selection regimes still remains quite elusive. The analysis of individual bacterial models cannot adequately explore this topic. Therefore, we used primary sequence data and secondary structures of a mitochondrial 16S rRNA fragment of 558 insect species from 15 monophyletic groups to study patterns of sequence variation, and variation of secondary structure. Using simulation studies to establish significance levels of change, we found that despite conservation of secondary structure, the location of sequence variation within the conserved rRNA structure changes significantly between groups of insects. Despite our conservative estimation procedure we found significant site-specific rate changes at 56 sites out of 184. Additionally, site-specific rate variation is somewhat clustered in certain helices. Both results confirm what has been predicted from an application of non-stationary maximum likelihood models to rRNA sequences. Clearly, constraints on sequence variation evolve and leave footprints in the form of evolutionary plasticity in rRNA sequences. Here, we show that a better understanding of the evolution of rRNA sequences can be obtained by integrating both phylogenetic and structural information.
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