Dynamic evolution of base composition: causes and consequences in avian phylogenomics.

Abstract

Resolving the phylogenetic relationships among birds is a classical problem in systematics, and this is particularly so when it comes to understanding the relationships among Neoaves. Previous phylogenetic inference of birds has been limited to mitochondrial genomes or a few nuclear genes. Here, we apply deep brain transcriptome sequencing of nine bird species (several passerines, hummingbirds, dove, parrot, and emu), using next-generation sequencing technology to understand features of transcriptome evolution in birds and how this affects phylogenetic inference, and combine with data from two bird species using first generation technology. The phylogenomic data matrix comprises 1,995 genes and a total of 0.77 Mb of exonic sequence. First, we find an unexpected heterogeneity in the evolution of base composition among avian lineages. There is a pronounced increase in guanine + cytosine (GC) content in the third codon position in several independent lineages, with the strongest effect seen in passerines. Second, we evaluate the effect of GC content variation on phylogenetic reconstruction. We find important inconsistencies between the topologies obtained with or without taking GC variation into account, each supporting different conclusions of past studies and also influencing hypotheses on the evolution of the trait of vocal learning. Third, we demonstrate a link between GC content evolution and recombination rate and, focusing on the zebra finch lineage, find that recombination seems to drive GC content. Although we cannot reveal the causal relationships, this observation is consistent with the model of GC-biased gene conversion. Finally, we use this unparalleled amount of avian sequence data to study the rate of molecular evolution, calibrated by fossil evidence and augmented with data from alligator transcriptome sequencing. There is a 2- to 3-fold variation in substitution rate among lineages with passerines being the most rapidly evolving and ratites the slowest. This study illustrates the potential of next-generation sequencing for phylogenomic studies but also the pitfalls when using genome-wide data with heterogeneous base composition.

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Published Version (Please cite this version)

10.1093/molbev/msr047

Publication Info

Nabholz, Benoit, Axel Künstner, Rui Wang, Erich D Jarvis and Hans Ellegren (2011). Dynamic evolution of base composition: causes and consequences in avian phylogenomics. Mol Biol Evol, 28(8). pp. 2197–2210. 10.1093/molbev/msr047 Retrieved from https://hdl.handle.net/10161/11239.

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Scholars@Duke

Jarvis

Erich David Jarvis

Adjunct Professor in the Deptartment of Neurobiology

Dr. Jarvis' laboratory studies the neurobiology of vocal communication. Emphasis is placed on the molecular pathways involved in the perception and production of learned vocalizations. They use an integrative approach that combines behavioral, anatomical, electrophysiological and molecular biological techniques. The main animal model used is songbirds, one of the few vertebrate groups that evolved the ability to learn vocalizations. The generality of the discoveries is tested in other vocal learning orders, such as parrots and hummingbirds, as well as non-vocal learners, such as pigeons and non-human primates. Some of the questions require performing behavior/molecular biology experiments in freely ranging animals, such as hummingbirds in tropical forest of Brazil. Recent results show that in songbirds, parrots and hummingbirds, perception and production of song are accompanied by anatomically distinct patterns of gene expression. All three groups were found to exhibit vocally-activated gene expression in exactly 7 forebrain nuclei that are very similar to each other. These structures for vocal learning and production are thought to have evolved independently within the past 70 million years, since they are absent from interrelated non-vocal learning orders. One structure, Area X of the basal ganglia's striatum in songbirds, shows large differential gene activation depending on the social context in which the bird sings. These differences may reflect a semantic content of song, perhaps similar to human language.

The overall goal of the research is to advance knowledge of the neural mechanisms for vocal learning and basic mechanisms of brain function. These goals are further achieved by combined collaborative efforts with the laboratories of Drs. Mooney and Nowicki at Duke University, who study respectively behavior and electrophysiological aspects of songbird vocal communication.


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