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The meiotic cohesin subunit REC8 contributes to multigenic adaptive evolution of autopolyploid meiosis in Arabidopsis arenosa.

Chris MorganEmilie KnightKirsten Bomblies
Published in: PLoS genetics (2022)
Genome duplication, which leads to polyploidy, poses challenges to the meiotic segregation of the now-multiple homologous chromosome copies. Genome scan data showed previously that adaptation to polyploid meiosis in autotetraploid Arabidopsis arenosa is likely multigenic, involving genes encoding interacting proteins. But what does this really mean? Functional follow-up studies to genome scans for multigenic traits remain rare in most systems, and thus many mysteries remain about the "functional architecture" of polygenic adaptations. Do different genes all contribute subtle and additive progression towards a fitness optimum, or are there more complex interactions? We previously showed that derived alleles of genes encoding two interacting meiotic axis proteins (ASY1 and ASY3) have additive functional consequences for meiotic adaptation. Here we study derived versus ancestral alleles of the meiotic cohesin subunit REC8, which has roles in chromatin condensation, recruiting the axes, and other critical functions in meiosis. We use genetic and cytological approaches to assess the functional effects of REC8 diploid versus tetraploid alleles, as well as their interaction with ancestral versus derived alleles of ASY1 and ASY3. We show that homozygotes for derived (tetraploid) REC8 alleles have significantly fewer unpaired univalents, a common problem in neotetraploids. Interactions with ASY1 and ASY3 are complex, with the genes in some cases affecting distinct traits, and additive or even antagonistic effects on others. These findings suggest that the road to meiotic adaptation in A. arenosa was perhaps neither straight nor smooth.
Keyphrases
  • genome wide
  • dna methylation
  • copy number
  • transcription factor
  • computed tomography
  • dna damage
  • body composition
  • magnetic resonance imaging
  • machine learning
  • dna repair
  • deep learning
  • big data
  • contrast enhanced