a huge fraction of the genome consists of dead transposons
assuming the model is correct, different cells will have different numbers of live transposons
The first point makes it difficult-in-general to count transposons in the genome, especially with high-throughput sequencing (HTS). HTS usually breaks the genome into small pieces, sequences them separately, then computationally reconstructs the whole thing. But if there’s many copies of similar sequence, this strategy is prone to err/uncertainty, and that’s exactly the case for all those transposon-copies.
That said, tools for reliably sequencing transposons are an active research area and progress is being made, so it will probably be cheaper in the not-too-distant future.
One way to circumvent this whole issue is to look at the amount of transposon RNA in a cell, rather than DNA. This doesn’t tell us anything about live transposon count—there could be a bunch of fresh copies which are being suppressed in a healthy cell. But it will tell us how active the transposons are right now. In practice, I expect this would mainly measure senescent cells (since they’re the only cells where I’d expect lots of transposon RNA), but that’s a hypothesis which would be useful to test.
I see, thanks. Could approaches like Horvath’s phenoage/grimage (which I think go after methylation) be good enough proxies for the “transposon clock” or somewhat correlated but different things?
Methylation is the primary transposon suppression mechanism, so methylation levels would tell us the extent to which transposons are suppressed at a given instant, but not the number of live transposon copies.
Good question.
The problem is difficult for two main reasons:
a huge fraction of the genome consists of dead transposons
assuming the model is correct, different cells will have different numbers of live transposons
The first point makes it difficult-in-general to count transposons in the genome, especially with high-throughput sequencing (HTS). HTS usually breaks the genome into small pieces, sequences them separately, then computationally reconstructs the whole thing. But if there’s many copies of similar sequence, this strategy is prone to err/uncertainty, and that’s exactly the case for all those transposon-copies.
That said, tools for reliably sequencing transposons are an active research area and progress is being made, so it will probably be cheaper in the not-too-distant future.
One way to circumvent this whole issue is to look at the amount of transposon RNA in a cell, rather than DNA. This doesn’t tell us anything about live transposon count—there could be a bunch of fresh copies which are being suppressed in a healthy cell. But it will tell us how active the transposons are right now. In practice, I expect this would mainly measure senescent cells (since they’re the only cells where I’d expect lots of transposon RNA), but that’s a hypothesis which would be useful to test.
I see, thanks. Could approaches like Horvath’s phenoage/grimage (which I think go after methylation) be good enough proxies for the “transposon clock” or somewhat correlated but different things?
Methylation is the primary transposon suppression mechanism, so methylation levels would tell us the extent to which transposons are suppressed at a given instant, but not the number of live transposon copies.