Deutsch
 
Benutzerhandbuch Datenschutzhinweis Impressum Kontakt
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

A novel approach identifies the first transcriptome networks in bats: A new genetic model for vocal communication

MPG-Autoren
/persons/resource/persons73285

Rodenas-Cuadrado,  Pedro
Language and Genetics Department, MPI for Psycholinguistics, Max Planck Society;

/persons/resource/persons61183

Chen,  Xiaowei Sylvia
Language and Genetics Department, MPI for Psycholinguistics, Max Planck Society;

/persons/resource/persons37905

Vernes,  Sonja C.
Language and Genetics Department, MPI for Psycholinguistics, Max Planck Society;
Donders Institute for Brain, Cognition and Behaviour, External Organizations;
Neurogenetics of Vocal Communication Group, MPI for Psycholinguistics, Max Planck Society;

Externe Ressourcen
Volltexte (frei zugänglich)

s12864-015-2068-1.pdf
(Verlagsversion), 5MB

Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Rodenas-Cuadrado, P., Chen, X. S., Wiegrebe, L., Firzlaff, U., & Vernes, S. C. (2015). A novel approach identifies the first transcriptome networks in bats: A new genetic model for vocal communication. BMC Genomics, 16: 836. doi:10.1186/s12864-015-2068-1.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0028-93A5-8
Zusammenfassung
Background Bats are able to employ an astonishingly complex vocal repertoire for navigating their environment and conveying social information. A handful of species also show evidence for vocal learning, an extremely rare ability shared only with humans and few other animals. However, despite their potential for the study of vocal communication, bats remain severely understudied at a molecular level. To address this fundamental gap we performed the first transcriptome profiling and genetic interrogation of molecular networks in the brain of a highly vocal bat species, Phyllostomus discolor. Results Gene network analysis typically needs large sample sizes for correct clustering, this can be prohibitive where samples are limited, such as in this study. To overcome this, we developed a novel bioinformatics methodology for identifying robust co-expression gene networks using few samples (N=6). Using this approach, we identified tissue-specific functional gene networks from the bat PAG, a brain region fundamental for mammalian vocalisation. The most highly connected network identified represented a cluster of genes involved in glutamatergic synaptic transmission. Glutamatergic receptors play a significant role in vocalisation from the PAG, suggesting that this gene network may be mechanistically important for vocal-motor control in mammals. Conclusion We have developed an innovative approach to cluster co-expressing gene networks and show that it is highly effective in detecting robust functional gene networks with limited sample sizes. Moreover, this work represents the first gene network analysis performed in a bat brain and establishes bats as a novel, tractable model system for understanding the genetics of vocal mammalian communication.