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APOK3, a pollen killer antidote in Arabidopsis thaliana.

Matthieu SimonStéphanie DurandAnthony RicouNathalie VrielynckBaptiste MayjonadeJérôme GouzyRoxane BoyerFabrice RouxChristine CamilleriFrançoise Budar
Published in: Genetics (2022)
The principles of heredity state that the two alleles carried by a heterozygote are equally transmitted to the progeny. However, genomic regions that escape this rule have been reported in many organisms. It is notably the case of genetic loci referred to as gamete killers, where one allele enhances its transmission by causing the death of the gametes that do not carry it. Gamete killers are of great interest, particularly to understand mechanisms of evolution and speciation. Although being common in plants, only a few, all in rice, have so far been deciphered to the causal genes. Here, we studied a pollen killer found in hybrids between two accessions of Arabidopsis thaliana. Exploring natural variation, we observed this pollen killer in many crosses within the species. Genetic analyses revealed that three genetically linked elements are necessary for pollen killer activity. Using mutants, we showed that this pollen killer works according to a poison-antidote model, where the poison kills pollen grains not producing the antidote. We identified the gene encoding the antidote, a chimeric protein addressed to mitochondria. De novo genomic sequencing in 12 natural variants with different behaviors regarding the pollen killer revealed a hyper variable locus, with important structural variations particularly in killer genotypes, where the antidote gene recently underwent duplications. Our results strongly suggest that the gene has newly evolved within A. thaliana. Finally, we identified in the protein sequence polymorphisms related to its antidote activity.
Keyphrases
  • copy number
  • genome wide
  • arabidopsis thaliana
  • dna methylation
  • genome wide identification
  • single cell
  • stem cells
  • cell death
  • gene expression
  • binding protein
  • reactive oxygen species
  • bioinformatics analysis