New study shows global impact of genomic imbalance on gene expression in Drosophila
Oct. 24, 2013
A longstanding area of investigation of the Birchler lab involves the balance of gene regulatory mechanisms. Studies in his lab have shown that changing the balance (stoichiometry) of individual components of regulatory complexes affects target gene expression, which is manifested in chromosomal dosage series. The most common such dosage effect is an inverse correlation between the dosage of a chromosomal segment or individual regulator and the amount of target gene expression. This “inverse dosage” effect is likely to contribute to the molecular basis of aneuploid syndromes. When a regulatory dosage change is combined on the same chromosomal segment as a target gene, the target will exhibit dosage compensation. This type of dosage effect appears to be responsible for X chromosomal dosage compensation in Drosophila.
In a new paper published in the September 23 issue of the Proceedings of the National Academy of Sciences, the Birchler lab reports on the findings from an RNA sequencing project to examine global patterns of gene expression in male and female Drosophila larvae trisomic for the left arm of chromosome 2 (2L). According to the authors, “the study demonstrates a global impact of genomic imbalance on gene expression and the generality of the inverse dosage effect and aneuploid dosage compensation. The study indicates that sex chromosomes and genes with sex-biased expression evolve distinctly in response to dosage-sensitive regulation.”
The paper, titled “Differential effect of aneuploidy on the X chromosome and genes with sex-biased expression in Drosophila,” is coauthored by Dr. Lin Sun (postdoctoral fellow), Adam Johnson (PhD student) and Aaron Lambdin (undergraduate student) from the Division of Biological Sciences and Dr. Jianlin Cheng and Jilong Li from the Department of Computer Sciences.
Written by: Melody Kroll
News by research strength
- Cell Biology
- Genetics & Genomics
- Molecular Biology
- Plant Biology
- Quantitative & Computational Biology