Anyone ever making or working with transgenics constructed with transposon vectors has experienced “position effects”. Position effects refers to variation in transgene expression as a function of integration site location. This can make comparisons of different transgenes in different locations very difficult and site-specific integration systems were created largely in response to unwanted position effects.
But position effects can also be highly desirable. In fact, sensor systems have been devised in order to intentionally detect these effects. For example, enhancer trapping uses local variation in transgene expression to locate enhancers. The term “position effects” sound good but it is a bit of a black box. Clearly there are multiple factors that go into this box – local enhancers and promoters, as just mentioned, but also chromatin structure, methylation patterns and probably other things as well. Some specific cases of position effects have been well studied and understood – variegation of white in Drosophila melanogaster by expansion and contractions of local heterochromatin, for example.
A genome wide analysis of position effects on transgene expression has never been done, for technical reasons although there have been efforts that have looked at small collections of integrated elements. Now Akhtar et al (2014) describe a method for simultaneously generating and analyzing thousands of reporters integrated in parallel (TRIP) at a genome-wide level.
The authors demonstrate TRIP in mouse ES cells using a piggyBac vector. The basic idea is to create many barcoded vectors (thousands) containing a reported gene, transfect into cells, stimulate integration and then analyze. Analysis is done on transfected cells en masse. iPCR is used to determine integration sites and RNAseq is used to detect and quantitate expression. Expression data are linked to location data because the barcodes were situated in the vector so that they appear in the iPCR data and the cDNA.
One ends up with a genome map of expression levels as a function of genomic position. Of course the observed patterns stimulate lots of questions.
The authors describe how this basic strategy can be used in a variety of ways.
Akhtar W, Pindyurin AV, de Jong J, Pagie L, ten Hoeve J, Berns A, Wessels LFA, van Steensel B, van Lohuizen M (2014) Using TRIP for genome-wide position effect analysis in cultured cells. Nat Protocols 9: 1255-1281 doi:10.1038/nprot.2014.072 Published online 08 May 2014