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RNAi phenotypes are influenced by the genetic background of the injected strain

BACKGROUND: RNA interference (RNAi) is a powerful tool to study gene function in organisms that are not amenable to classical forward genetics. Hence, together with the ease of comprehensively identifying genes by new generation sequencing, RNAi is expanding the scope of animal species and questions...

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Detalles Bibliográficos
Autores principales: Kitzmann, Peter, Schwirz, Jonas, Schmitt-Engel, Christian, Bucher, Gregor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3574008/
https://www.ncbi.nlm.nih.gov/pubmed/23324472
http://dx.doi.org/10.1186/1471-2164-14-5
Descripción
Sumario:BACKGROUND: RNA interference (RNAi) is a powerful tool to study gene function in organisms that are not amenable to classical forward genetics. Hence, together with the ease of comprehensively identifying genes by new generation sequencing, RNAi is expanding the scope of animal species and questions that can be addressed in terms of gene function. In the case of genetic mutants, the genetic background of the strains used is known to influence the phenotype while this has not been described for RNAi experiments. RESULTS: Here we show in the red flour beetle Tribolium castaneum that RNAi against Tc-importin α1 leads to different phenotypes depending on the injected strain. We rule out off target effects and show that sequence divergence does not account for this difference. By quantitatively comparing phenotypes elicited by RNAi knockdown of four different genes we show that there is no general difference in RNAi sensitivity between these strains. Finally, we show that in case of Tc-importin α1 the difference depends on the maternal genotype. CONCLUSIONS: These results show that in RNAi experiments strain specific differences have to be considered and that a proper documentation of the injected strain is required. This is especially important for the increasing number of emerging model organisms that are being functionally investigated using RNAi. In addition, our work shows that RNAi is suitable to systematically identify the differences in the gene regulatory networks present in populations of the same species, which will allow novel insights into the evolution of animal diversity.