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Convergent transcriptional specializations in the brains of humans and song-learning birds.

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Date
2014-12-12
Authors
Pfenning, Andreas R
Hara, Erina
Whitney, Osceola
Rivas, Miriam V
Wang, Rui
Roulhac, Petra L
Howard, Jason T
Wirthlin, Morgan
Lovell, Peter V
Ganapathy, Ganeshkumar
Mouncastle, Jacquelyn
Moseley, M Arthur
Thompson, J Will
Soderblom, Erik J
Iriki, Atsushi
Kato, Masaki
Gilbert, M Thomas P
Zhang, Guojie
Bakken, Trygve
Bongaarts, Angie
Bernard, Amy
Lein, Ed
Mello, Claudio V
Hartemink, Alexander J
Jarvis, Erich D
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(25 total)
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Abstract
Song-learning birds and humans share independently evolved similarities in brain pathways for vocal learning that are essential for song and speech and are not found in most other species. Comparisons of brain transcriptomes of song-learning birds and humans relative to vocal nonlearners identified convergent gene expression specializations in specific song and speech brain regions of avian vocal learners and humans. The strongest shared profiles relate bird motor and striatal song-learning nuclei, respectively, with human laryngeal motor cortex and parts of the striatum that control speech production and learning. Most of the associated genes function in motor control and brain connectivity. Thus, convergent behavior and neural connectivity for a complex trait are associated with convergent specialized expression of multiple genes.
Type
Journal article
Subject
Adult
Animals
Birds
Brain
Brain Mapping
Corpus Striatum
Evolution, Molecular
Finches
Gene Expression Regulation
Humans
Learning
Male
Motor Cortex
Neural Pathways
Species Specificity
Speech
Transcription, Genetic
Transcriptome
Vocalization, Animal
Permalink
https://hdl.handle.net/10161/11149
Published Version (Please cite this version)
10.1126/science.1256846
Publication Info
Pfenning, Andreas R; Hara, Erina; Whitney, Osceola; Rivas, Miriam V; Wang, Rui; Roulhac, Petra L; ... Jarvis, Erich D (2014). Convergent transcriptional specializations in the brains of humans and song-learning birds. Science, 346(6215). pp. 1256846. 10.1126/science.1256846. Retrieved from https://hdl.handle.net/10161/11149.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
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Scholars@Duke

Hartemink

Alexander J. Hartemink

Professor in the Department of Computer Science
Computational biology, machine learning, Bayesian statistics, transcriptional regulation, genomics and epigenomics, graphical models, Bayesian networks, hidden Markov models, systems biology, computational neurobiology, classification, feature selection
Jarvis

Erich David Jarvis

Adjunct Professor in the Dept. 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
Moseley

Martin Arthur Moseley III

Adjunct Professor in the Department of Cell Biology

Erik James Soderblom

Assistant Research Professor of Cell Biology
Thompson

J. Will Thompson

Adjunct Assistant Professor in the Department of Pharmacology & Cancer Biology
Dr. Thompson's research focuses on the development and deployment of proteomics and metabolomics mass spectrometry techniques for the analysis of biological systems. He served as the Assistant Director of the Proteomics and Metabolomics Shared Resource in the Duke School of Medicine from 2007-2021. He currently maintains collaborations in metabolomics and proteomics research at Duke, and develops new tools for chemical analysis as a Princi
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