Back

Recent transposable element bursts triggered by insertions near genes in a fungal pathogen

Oggenfuss, U.; Croll, D.

2022-07-13 genomics
10.1101/2022.07.13.499862 bioRxiv
Show abstract

The activity of transposable elements (TEs) contributes significantly to genome evolution. TEs often destabilize genome integrity but may also confer adaptive variation in phenotypic traits. De-repression of epigenetically silenced TEs often initiates bursts of transposition activity that may be counteracted by purifying selection and genome defenses. However, how these forces interact to determine the expansion routes of TEs within a species remains largely unknown. Here, we analyzed a set of 19 telomere-to-telomere genomes of the fungal wheat pathogen Zymoseptoria tritici. Phylogenetic reconstruction and ancestral state estimates of individual TE families revealed that TEs have undergone distinct activation and repression periods resulting in highly uneven copy numbers between genomes of the same species. Most TEs are clustered in gene poor niches, indicating strong purifying selection against insertions near coding sequences. TE families with high copy numbers have low sequence divergence and strong signatures of defense mechanisms (i.e., RIP). In contrast, small non-autonomous TEs (i.e., MITEs) are less impacted by defense mechanisms and are often located in close proximity to genes. Individual TE families have experienced multiple distinct burst events that generated many nearly identical copies. We found that a Copia element burst was initiated from recent copies inserted substantially closer to genes compared to older insertions. Overall, TE bursts tended to initiate from copies in GC-rich niches that escaped inactivation by genomic defenses. Our work shows how specific genomic environments features provide triggers for TE proliferation.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.