Ye et al. in a just-published paper in Scientific Reports address some questions regarding transgene expression in Bombyx mori that are likely to be of interest to others working with transgenic insects.
The relatively random patterns of transposable element vector integration often results in independent lines being created with the transgene in different genomic locations. Expression levels will vary due to ‘position effects’ – a catch-all term for anything in the genome that differentially impacts gene expression such as local chromatin structure or local promoters or enhancers.
Ye et al. frame their work within the context of this position effect problem and the challenges associated with trying to assess which line has expression levels of your transgenes that you are most interested in – often the one with the ‘strongest’ expression.
Ye and colleagues analyze the sericin (Ser1) promoter in Bombyx mori. They report a couple of findings that will probably be of interest to anyone using transgenic technologies in insects.
A reasonable strategy for increasing promoter strength is to increase the number of enhancer binding sites 5’ of the core promoter. This has worked in a number of commonly used systems such as the Gal4 /UAS system. Ye et al. found that not only did multimerizing Ser1 proximal promoter sequences not increase promoter activity but it resulted in progressively less promoter activity.
Most transgene packages contained on a vector consist of a dominant visible marker gene regulated by a well characterized and usually strong promoter, and the transgene of interest is regulated with some other promoter. Ye et al. asked if the levels of expression of those two genes was correlated. Could the strength of marker gene expression be used as a proxy for the strength of transgene expression? If marker gene expression was low or otherwise affected by position effects, was transgene expression also low or similarly affected?
Ye et al. found that with the vectors and promoters they used (Ser1 and Fib-L, fibroin light-chain) the expression levels of the two genes were highly correlated – Pearson’s correlation r = 0.8050 for one series of transgenics (n~40) , r = 0.6382 in a second series of transgenics (n~25)
Finally, Ye et al. measured both transcript (qRTPCR) and protein levels (ELISA) associated with EGFP expression and found them to highly correlated (r = 0.9275, n=6).
The results reported in this paper are interesting but I would have liked to have seen more discussion about results from other insect systems that put their results in a broader context. In my lab we have seen marker genes and transgenes in mosquitioes seemingly affected by position in different ways and degrees, but we have not done the careful quantitation that Ye et al. have done.
Nonetheless if you work with transgenic insects and use transposon vectors then you have considered many of the questions Ye et al. address and you might find this paper of some interest.
Ye L, Qian Q, Zhang Y, You Z, Che J, Song J, Zhong B: Analysis of the sericin1 promoter and assisted detection of exogenous gene expression efficiency in the silkworm Bombyx mori L. Sci Rep 2015, 5. doi 10.1038/srep08301